## Awesome Deep Learning papers for industrial Search, Recommendation and Advertisement. They focus on Embedding, Matching, Pre-Ranking, Ranking (CTR/CVR prediction), Post-Ranking, Relevance-Ranking, LLM based ranking, Reinforcement Learning and so on.
#### Please cite our paper: [Deep Learning to Rank in Industrial Search Engines, Recommender Systems, and Online Advertising - An Overview and New Perspectives](https://dl.acm.org/doi/full/10.1145/3797895), which was published in TOIS 2026.

## 00_Overview
* [2026 (TOIS) [DLTR] Deep Learning to Rank in Industrial Search Engines, Recommender Systems, and Online Advertising - An Overview and New Perspectives](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/00_Overview/2026%20%28TOIS%29%20%5BDLTR%5D%20Deep%20Learning%20to%20Rank%20in%20Industrial%20Search%20Engines%2C%20Recommender%20Systems%2C%20and%20Online%20Advertising%20-%20An%20Overview%20and%20New%20Perspectives.pdf) <br />

## 01_Embedding
* [2013 (Google) (NIPS) [Word2vec] Distributed Representations of Words and Phrases and their Compositionality](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/01_Embedding/2013%20%28Google%29%20%28NIPS%29%20%5BWord2vec%5D%20Distributed%20Representations%20of%20Words%20and%20Phrases%20and%20their%20Compositionality.pdf) <br />
* [2014 (KDD) [DeepWalk]  DeepWalk - online learning of social representations](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/01_Embedding/2014%20%28KDD%29%20%5BDeepWalk%5D%20%20DeepWalk%20-%20online%20learning%20of%20social%20representations.pdf) <br />
* [2015 (WWW) [LINE] LINE Large-scale Information Network Embedding](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/01_Embedding/2015%20%28WWW%29%20%5BLINE%5D%20LINE%20Large-scale%20Information%20Network%20Embedding.pdf) <br />
* [2016 (KDD) [Node2vec] node2vec - Scalable Feature Learning for Networks](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/01_Embedding/2016%20%28KDD%29%20%5BNode2vec%5D%20node2vec%20-%20Scalable%20Feature%20Learning%20for%20Networks.pdf) <br />
* [2017 (ICLR) [GCN] Semi-supervised Classification with Graph Convolutional Networks ](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/01_Embedding/2017%20%28ICLR%29%20%5BGCN%5D%20Semi-supervised%20Classification%20with%20Graph%20Convolutional%20Networks%20.pdf) <br />
* [2017 (KDD) [Struc2vec] struc2vec - Learning Node Representations from Structural Identity](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/01_Embedding/2017%20%28KDD%29%20%5BStruc2vec%5D%20struc2vec%20-%20Learning%20Node%20Representations%20from%20Structural%20Identity.pdf) <br />
* [2017 (NIPS) [GraphSAGE] Inductive Representation Learning on Large Graphs](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/01_Embedding/2017%20%28NIPS%29%20%5BGraphSAGE%5D%20Inductive%20Representation%20Learning%20on%20Large%20Graphs.pdf) <br />
* [2018 (Alibaba) (KDD) *[Alibaba Embedding] Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/01_Embedding/2018%20%28Alibaba%29%20%28KDD%29%20%2A%5BAlibaba%20Embedding%5D%20Billion-scale%20Commodity%20Embedding%20for%20E-commerce%20Recommendation%20in%20Alibaba.pdf) <br />
* [2018 (ICLR) [GAT]  Graph Attention Networks](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/01_Embedding/2018%20%28ICLR%29%20%5BGAT%5D%20%20Graph%20Attention%20Networks.pdf) <br />
* [2018 (Pinterest) (KDD) *[PinSage] Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/01_Embedding/2018%20%28Pinterest%29%20%28KDD%29%20%2A%5BPinSage%5D%20Graph%20Convolutional%20Neural%20Networks%20for%20Web-Scale%20Recommender%20Systems.pdf) <br />
* [2018 (WSDM) [NetMF] Network embedding as matrix factorization - Unifying deepwalk, line, pte, and node2vec](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/01_Embedding/2018%20%28WSDM%29%20%5BNetMF%5D%20Network%20embedding%20as%20matrix%20factorization%20-%20Unifying%20deepwalk%2C%20line%2C%20pte%2C%20and%20node2vec.pdf) <br />
* [2019 (Alibaba) (KDD) *[GATNE] Representation Learning for Attributed Multiplex Heterogeneous Network](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/01_Embedding/2019%20%28Alibaba%29%20%28KDD%29%20%2A%5BGATNE%5D%20Representation%20Learning%20for%20Attributed%20Multiplex%20Heterogeneous%20Network.pdf) <br />

## 02_Matching
* [1994 (CSCW) [User-CF] GroupLens - An Open Architecture for Collaborative Filtering of Netnews](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/1994%20%28CSCW%29%20%5BUser-CF%5D%20GroupLens%20-%20An%20Open%20Architecture%20for%20Collaborative%20Filtering%20of%20Netnews.pdf) <br />
* [1998 (Microsoft) Empirical Analysis of Predictive Algorithms for Collaborative Filtering](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/1998%20%28Microsoft%29%20Empirical%20Analysis%20of%20Predictive%20Algorithms%20for%20Collaborative%20Filtering.pdf) <br />
* [2003 (Amazon) [Item-CF] Amazon.com recommendations - item-to-item collaborative filtering](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2003%20%28Amazon%29%20%5BItem-CF%5D%20Amazon.com%20recommendations%20-%20item-to-item%20collaborative%20filtering.pdf) <br />
* [2009 (Computer) [MF] Matrix factorization techniques for recommender systems](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2009%20%28Computer%29%20%5BMF%5D%20Matrix%20factorization%20techniques%20for%20recommender%20systems.pdf) <br />
* [2013 (Microsoft) (CIKM) [DSSM] Learning deep structured semantic models for web search using clickthrough data](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2013%20%28Microsoft%29%20%28CIKM%29%20%5BDSSM%5D%20Learning%20deep%20structured%20semantic%20models%20for%20web%20search%20using%20clickthrough%20data.pdf) <br />
* [2015 (KDD) [Sceptre] Inferring Networks of Substitutable and Complementary Products](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2015%20%28KDD%29%20%5BSceptre%5D%20Inferring%20Networks%20of%20Substitutable%20and%20Complementary%20Products.pdf) <br />
* [2016 (Google) (RecSys) **[Youtube DNN] Deep Neural Networks for YouTube Recommendations](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2016%20%28Google%29%20%28RecSys%29%20%2A%2A%5BYoutube%20DNN%5D%20Deep%20Neural%20Networks%20for%20YouTube%20Recommendations.pdf) <br />
* [2018 (Airbnb) (KDD) *[Airbnb Embedding] Real-time Personalization using Embeddings for Search Ranking at Airbnb](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2018%20%28Airbnb%29%20%28KDD%29%20%2A%5BAirbnb%20Embedding%5D%20Real-time%20Personalization%20using%20Embeddings%20for%20Search%20Ranking%20at%20Airbnb.pdf) <br />
* [2018 (Alibaba) (KDD) * [TDM] Learning Tree-based Deep Model for Recommender Systems](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2018%20%28Alibaba%29%20%28KDD%29%20%2A%20%5BTDM%5D%20Learning%20Tree-based%20Deep%20Model%20for%20Recommender%20Systems.pdf) <br />
* [2018 (Pinterest) (KDD) *[PinSage] Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2018%20%28Pinterest%29%20%28KDD%29%20%2A%5BPinSage%5D%20Graph%20Convolutional%20Neural%20Networks%20for%20Web-Scale%20Recommender%20Systems.pdf) <br />
* [2019 (Alibaba) (CIKM) **[MIND] Multi-Interest Network with Dynamic Routing for Recommendation at Tmall](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2019%20%28Alibaba%29%20%28CIKM%29%20%2A%2A%5BMIND%5D%20Multi-Interest%20Network%20with%20Dynamic%20Routing%20for%20Recommendation%20at%20Tmall.pdf) <br />
* [2019 (Alibaba) (CIKM) *[SDM] SDM - Sequential deep matching model for online large-scale recommender system](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2019%20%28Alibaba%29%20%28CIKM%29%20%2A%5BSDM%5D%20SDM%20-%20Sequential%20deep%20matching%20model%20for%20online%20large-scale%20recommender%20system.pdf) <br />
* [2019 (Alibaba) (NIPS) *[JTM] Joint Optimization of Tree-based Index and Deep Model for Recommender Systems](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2019%20%28Alibaba%29%20%28NIPS%29%20%2A%5BJTM%5D%20Joint%20Optimization%20of%20Tree-based%20Index%20and%20Deep%20Model%20for%20Recommender%20Systems.pdf) <br />
* [2019 (Amazon) (KDD) Semantic Product Search](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2019%20%28Amazon%29%20%28KDD%29%20Semantic%20Product%20Search.pdf) <br />
* [2019 (Baidu) (KDD) *[MOBIUS] MOBIUS - Towards the Next Generation of Query-Ad Matching in Baidu's Sponsored Search](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2019%20%28Baidu%29%20%28KDD%29%20%2A%5BMOBIUS%5D%20MOBIUS%20-%20Towards%20the%20Next%20Generation%20of%20Query-Ad%20Matching%20in%20Baidu%27s%20Sponsored%20Search.pdf) <br />
* [2019 (Google) (RecSys) **[Two-Tower] Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2019%20%28Google%29%20%28RecSys%29%20%2A%2A%5BTwo-Tower%5D%20Sampling-Bias-Corrected%20Neural%20Modeling%20for%20Large%20Corpus%20Item%20Recommendations.pdf) <br />
* [2019 (Google) (WSDM) *[Top-K Off-Policy] Top-K Off-Policy Correction for a REINFORCE Recommender System](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2019%20%28Google%29%20%28WSDM%29%20%2A%5BTop-K%20Off-Policy%5D%20Top-K%20Off-Policy%20Correction%20for%20a%20REINFORCE%20Recommender%20System.pdf) <br />
* [2019 [Tencent] (KDD) A User-Centered Concept Mining System for Query and Document Understanding at Tencent](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2019%20%5BTencent%5D%20%28KDD%29%20A%20User-Centered%20Concept%20Mining%20System%20for%20Query%20and%20Document%20Understanding%20at%20Tencent.pdf) <br />
* [2020 (Alibaba) (Arxiv) [SWING] Large Scale Product Graph Construction for Recommendation in E-commerce](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2020%20%28Alibaba%29%20%28Arxiv%29%20%5BSWING%5D%20Large%20Scale%20Product%20Graph%20Construction%20for%20Recommendation%20in%20E-commerce.pdf) <br />
* [2020 (Alibaba) (ICML) [OTM] Learning Optimal Tree Models under Beam Search](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2020%20%28Alibaba%29%20%28ICML%29%20%5BOTM%5D%20Learning%20Optimal%20Tree%20Models%20under%20Beam%20Search.pdf) <br />
* [2020 (Alibaba) (KDD) *[ComiRec] Controllable Multi-Interest Framework for Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2020%20%28Alibaba%29%20%28KDD%29%20%2A%5BComiRec%5D%20Controllable%20Multi-Interest%20Framework%20for%20Recommendation.pdf) <br />
* [2020 (Facebook) (KDD) **[Embedding for Facebook Search] Embedding-based Retrieval in Facebook Search](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2020%20%28Facebook%29%20%28KDD%29%20%2A%2A%5BEmbedding%20for%20Facebook%20Search%5D%20Embedding-based%20Retrieval%20in%20Facebook%20Search.pdf) <br />
* [2020 (Google) (WWW) *[MNS] Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2020%20%28Google%29%20%28WWW%29%20%2A%5BMNS%5D%20Mixed%20Negative%20Sampling%20for%20Learning%20Two-tower%20Neural%20Networks%20in%20Recommendations.pdf) <br />
* [2020 (JD) (CIKM) *[DecGCN] Decoupled Graph Convolution Network for Inferring Substitutable and Complementary Items](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2020%20%28JD%29%20%28CIKM%29%20%2A%5BDecGCN%5D%20Decoupled%20Graph%20Convolution%20Network%20for%20Inferring%20Substitutable%20and%20Complementary%20Items.pdf) <br />
* [2020 (JD) (SIGIR) [DPSR] Towards Personalized and Semantic Retrieval - An End-to-EndSolution for E-commerce Search via Embedding Learning](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2020%20%28JD%29%20%28SIGIR%29%20%5BDPSR%5D%20Towards%20Personalized%20and%20Semantic%20Retrieval%20-%20An%20End-to-EndSolution%20for%20E-commerce%20Search%20via%20Embedding%20Learning.pdf) <br />
* [2020 (Microsoft) (Arxiv) TwinBERT - Distilling Knowledge to Twin-Structured BERT Models for Efficient Retrieval](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2020%20%28Microsoft%29%20%28Arxiv%29%20TwinBERT%20-%20Distilling%20Knowledge%20to%20Twin-Structured%20BERT%20Models%20for%20Efficient%20Retrieval.pdf) <br />
* [2021 (Alibaba) (KDD) *  [MGDSPR] Embedding-based Product Retrieval in Taobao Search](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2021%20%28Alibaba%29%20%28KDD%29%20%2A%20%20%5BMGDSPR%5D%20Embedding-based%20Product%20Retrieval%20in%20Taobao%20Search.pdf) <br />
* [2021 (Alibaba) (SIGIR) * [PDN] Path-based Deep Network for Candidate Item Matching in Recommenders](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2021%20%28Alibaba%29%20%28SIGIR%29%20%2A%20%5BPDN%5D%20Path-based%20Deep%20Network%20for%20Candidate%20Item%20Matching%20in%20Recommenders.pdf) <br />
* [2021 (Amazon) (KDD) Extreme Multi-label Learning for Semantic Matching in Product Search](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2021%20%28Amazon%29%20%28KDD%29%20Extreme%20Multi-label%20Learning%20for%20Semantic%20Matching%20in%20Product%20Search.pdf) <br />
* [2021 (Baidu) (KDD) Pre-trained Language Model for Web-scale Retrieval in Baidu Search](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2021%20%28Baidu%29%20%28KDD%29%20Pre-trained%20Language%20Model%20for%20Web-scale%20Retrieval%20in%20Baidu%20Search.pdf) <br />
* [2021 (Bytedance) (Arxiv) [DR] Deep Retrieval - Learning A Retrievable Structure for Large-Scale Recommendations](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2021%20%28Bytedance%29%20%28Arxiv%29%20%5BDR%5D%20Deep%20Retrieval%20-%20Learning%20A%20Retrievable%20Structure%20for%20Large-Scale%20Recommendations.pdf) <br />
* [2021 (Meituan) (DLP-KDD) [DAT]A Dual Augmented Two-tower Model for Online Large-scale Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2021%20%28Meituan%29%20%28DLP-KDD%29%20%5BDAT%5DA%20Dual%20Augmented%20Two-tower%20Model%20for%20Online%20Large-scale%20Recommendation.pdf) <br />
* [2022 (Alibaba) (CIKM) **[NANN] Approximate Nearest Neighbor Search under Neural Similarity Metric for Large-Scale Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2022%20%28Alibaba%29%20%28CIKM%29%20%2A%2A%5BNANN%5D%20Approximate%20Nearest%20Neighbor%20Search%20under%20Neural%20Similarity%20Metric%20for%20Large-Scale%20Recommendation.pdf) <br />
* [2022 (Alibaba) (CIKM) [CLE-QR] Query Rewriting in TaoBao Search](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2022%20%28Alibaba%29%20%28CIKM%29%20%5BCLE-QR%5D%20Query%20Rewriting%20in%20TaoBao%20Search.pdf) <br />
* [2022 (Alibaba) **(CIKM) [MOPPR] Multi-Objective Personalized Product Retrieval in Taobao Search](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2022%20%28Alibaba%29%20%2A%2A%28CIKM%29%20%5BMOPPR%5D%20Multi-Objective%20Personalized%20Product%20Retrieval%20in%20Taobao%20Search.pdf) <br />
* [2024 (Bytedance) (KDD) [Trinity] Trinity - Syncretizing Multi-:Long-Tail:Long-Term Interests All in One](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2024%20%28Bytedance%29%20%28KDD%29%20%5BTrinity%5D%20Trinity%20-%20Syncretizing%20Multi-%3ALong-Tail%3ALong-Term%20Interests%20All%20in%20One.pdf) <br />
* [2024 (Meta) (Arxiv) ** [GR] Actions Speak Louder than Words - Trillion-Parameter Sequential Transducers for Generative Recommendations](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2024%20%28Meta%29%20%28Arxiv%29%20%2A%2A%20%5BGR%5D%20Actions%20Speak%20Louder%20than%20Words%20-%20Trillion-Parameter%20Sequential%20Transducers%20for%20Generative%20Recommendations.pdf) <br />
* [2025 (Bytedance) (Arxiv) [LongRetriever] LongRetriever - Towards Ultra-Long Sequence based Candidate Retrieval for Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2025%20%28Bytedance%29%20%28Arxiv%29%20%5BLongRetriever%5D%20LongRetriever%20-%20Towards%20Ultra-Long%20Sequence%20based%20Candidate%20Retrieval%20for%20Recommendation.pdf) <br />
* [2025 (Bytedance) (KDD) [VQ] Real-time Indexing for Large-scale Recommendation by Streaming Vector Quantization Retriever](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2025%20%28Bytedance%29%20%28KDD%29%20%5BVQ%5D%20Real-time%20Indexing%20for%20Large-scale%20Recommendation%20by%20Streaming%20Vector%20Quantization%20Retriever.pdf) <br />
* [2025 (JD) (KDD) [UniERF] UniERF - A Uniform Embedding-based Retrieval Framework for E-Commerce Search](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2025%20%28JD%29%20%28KDD%29%20%5BUniERF%5D%20UniERF%20-%20A%20Uniform%20Embedding-based%20Retrieval%20Framework%20for%20E-Commerce%20Search.pdf) <br />
* [2025 (Meta) (KDD) [MTMH] Optimizing Recall or Relevance? A Multi-Task Multi-Head Approach for Item-to-Item Retrieval in Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2025%20%28Meta%29%20%28KDD%29%20%5BMTMH%5D%20Optimizing%20Recall%20or%20Relevance%3F%20A%20Multi-Task%20Multi-Head%20Approach%20for%20Item-to-Item%20Retrieval%20in%20Recommendation.pdf) <br />
* [2025 (Meta) [RADAR ] RADAR - Recall Augmentation through Deferred Asynchronous Retrieval](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2025%20%28Meta%29%20%5BRADAR%20%5D%20RADAR%20-%20Recall%20Augmentation%20through%20Deferred%20Asynchronous%20Retrieval.pdf) <br />
* [2025 (Tencent) (Arxiv) An Efficient Embedding Based Ad Retrieval with GPU-Powered Feature Interaction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/2025%20%28Tencent%29%20%28Arxiv%29%20An%20Efficient%20Embedding%20Based%20Ad%20Retrieval%20with%20GPU-Powered%20Feature%20Interaction.pdf) <br />

#### ANN
* [2017 (Arxiv) (Meta) [FAISS] Billion-scale similarity search with GPUs](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/ANN/2017%20%28Arxiv%29%20%28Meta%29%20%5BFAISS%5D%20Billion-scale%20similarity%20search%20with%20GPUs.pdf) <br />
* [2020 (PAMI) [HNSW] Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/ANN/2020%20%28PAMI%29%20%5BHNSW%5D%20Efficient%20and%20Robust%20Approximate%20Nearest%20Neighbor%20Search%20Using%20Hierarchical%20Navigable%20Small%20World%20Graphs.pdf) <br />
* [2021 (TPAMI) [IVF-PQ] Product Quantization for Nearest Neighbor Search](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/ANN/2021%20%28TPAMI%29%20%5BIVF-PQ%5D%20Product%20Quantization%20for%20Nearest%20Neighbor%20Search.pdf) <br />

#### Graph_Neural_Networks
* [2017 (ICLR) [GCN] Semi-Supervised Classification with Graph Convolutional Networks](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/Graph_Neural_Networks/2017%20%28ICLR%29%20%5BGCN%5D%20Semi-Supervised%20Classification%20with%20Graph%20Convolutional%20Networks.pdf) <br />
* [2018 (ICLR) [GAT] Graph Attention Networks](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/Graph_Neural_Networks/2018%20%28ICLR%29%20%5BGAT%5D%20Graph%20Attention%20Networks.pdf) <br />
* [2018 (Pinterest) (KDD) [PinSage] Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/Graph_Neural_Networks/2018%20%28Pinterest%29%20%28KDD%29%20%5BPinSage%5D%20Graph%20Convolutional%20Neural%20Networks%20for%20Web-Scale%20Recommender%20Systems.pdf) <br />
* [2019 (Alibaba) (KDD) [IntentGC] IntentGC - a Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/Graph_Neural_Networks/2019%20%28Alibaba%29%20%28KDD%29%20%5BIntentGC%5D%20IntentGC%20-%20a%20Scalable%20Graph%20Convolution%20Framework%20Fusing%20Heterogeneous%20Information%20for%20Recommendation.pdf) <br />
* [2019 (Alibaba) (KDD) [MEIRec] Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/Graph_Neural_Networks/2019%20%28Alibaba%29%20%28KDD%29%20%5BMEIRec%5D%20Metapath-guided%20Heterogeneous%20Graph%20Neural%20Network%20for%20Intent%20Recommendation.pdf) <br />
* [2019 (Alibaba) (SIGIR) [GIN] Graph Intention Network for Click-through Rate Prediction in Sponsored Search](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/Graph_Neural_Networks/2019%20%28Alibaba%29%20%28SIGIR%29%20%5BGIN%5D%20Graph%20Intention%20Network%20for%20Click-through%20Rate%20Prediction%20in%20Sponsored%20Search.pdf) <br />
* [2020 (Alibaba) (SIGIR) [ATBRG] ATBRG - Adaptive Target-Behavior Relational Graph Network for Effective Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/Graph_Neural_Networks/2020%20%28Alibaba%29%20%28SIGIR%29%20%5BATBRG%5D%20ATBRG%20-%20Adaptive%20Target-Behavior%20Relational%20Graph%20Network%20for%20Effective%20Recommendation.pdf) <br />

#### LLM_Matching
* [2021 (Baidu) (KDD) Pre-trained Language Model for Web-scale Retrieval in Baidu Search](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/LLM_Matching/2021%20%28Baidu%29%20%28KDD%29%20Pre-trained%20Language%20Model%20for%20Web-scale%20Retrieval%20in%20Baidu%20Search.pdf) <br />
* [2023 (Google) (NIPS) [TIGER] Recommender Systems with Generative Retrieval](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/LLM_Matching/2023%20%28Google%29%20%28NIPS%29%20%5BTIGER%5D%20Recommender%20Systems%20with%20Generative%20Retrieval.pdf) <br />
* [2024 (Alibaba) (WWW) [BEQUE] Large Language Model based Long-tail Query Rewriting in Taobao Search](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/LLM_Matching/2024%20%28Alibaba%29%20%28WWW%29%20%5BBEQUE%5D%20Large%20Language%20Model%20based%20Long-tail%20Query%20Rewriting%20in%20Taobao%20Search.pdf) <br />
* [2024 (Kuaishou) (Arxiv) [KuaiFormer] KuaiFormer - Transformer-Based Retrieval at Kuaishou](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/LLM_Matching/2024%20%28Kuaishou%29%20%28Arxiv%29%20%5BKuaiFormer%5D%20KuaiFormer%20-%20Transformer-Based%20Retrieval%20at%20Kuaishou.pdf) <br />
* [2024 (Meta) (Arxiv) ** [GR] Actions Speak Louder than Words - Trillion-Parameter Sequential Transducers for Generative Recommendations](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/LLM_Matching/2024%20%28Meta%29%20%28Arxiv%29%20%2A%2A%20%5BGR%5D%20Actions%20Speak%20Louder%20than%20Words%20-%20Trillion-Parameter%20Sequential%20Transducers%20for%20Generative%20Recommendations.pdf) <br />
* [2024 (Meta) (Arxiv) Unifying Generative and Dense Retrieval for Sequential Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/LLM_Matching/2024%20%28Meta%29%20%28Arxiv%29%20Unifying%20Generative%20and%20Dense%20Retrieval%20for%20Sequential%20Recommendation.pdf) <br />
* [2024 (Xiaohongshu) (WWW) [NoteLLM] NoteLLM - Multimodal Large Representation Models for Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/LLM_Matching/2024%20%28Xiaohongshu%29%20%28WWW%29%20%5BNoteLLM%5D%20NoteLLM%20-%20Multimodal%20Large%20Representation%20Models%20for%20Recommendation.pdf) <br />
* [2025  (Kuaishou) (Arxiv)[OneRec] OneRec - Unifying Retrieve and Rank with Generative Recommender and Preference Alignment](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/LLM_Matching/2025%20%20%28Kuaishou%29%20%28Arxiv%29%5BOneRec%5D%20OneRec%20-%20Unifying%20Retrieve%20and%20Rank%20with%20Generative%20Recommender%20and%20Preference%20Alignment.pdf) <br />
* [2025 (Alibaba) (Arxiv) [TBGRecall] TBGRecall - A Generative Retrieval Model for E-commerce Recommendation Scenarios](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/LLM_Matching/2025%20%28Alibaba%29%20%28Arxiv%29%20%5BTBGRecall%5D%20TBGRecall%20-%20A%20Generative%20Retrieval%20Model%20for%20E-commerce%20Recommendation%20Scenarios.pdf) <br />
* [2025 (Arxiv) (Tencent) [RARE] Real-time Ad retrieval via LLM-generative Commercial Intention for Sponsored Search Advertising](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/LLM_Matching/2025%20%28Arxiv%29%20%28Tencent%29%20%5BRARE%5D%20Real-time%20Ad%20retrieval%20via%20LLM-generative%20Commercial%20Intention%20for%20Sponsored%20Search%20Advertising.pdf) <br />
* [2025 (Baidu) (Arxiv) [COBRA] Sparse Meets Dense -Unified Generative Recommendations with Cascaded Sparse-Dense Representations](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/LLM_Matching/2025%20%28Baidu%29%20%28Arxiv%29%20%5BCOBRA%5D%20Sparse%20Meets%20Dense%20-Unified%20Generative%20Recommendations%20with%20Cascaded%20Sparse-Dense%20Representations.pdf) <br />
* [2025 (Google) [PLUM] PLUM - Adapting Pre-trained Language Models for Industrial-scale Generative Recommendations](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/LLM_Matching/2025%20%28Google%29%20%5BPLUM%5D%20PLUM%20-%20Adapting%20Pre-trained%20Language%20Models%20for%20Industrial-scale%20Generative%20Recommendations.pdf) <br />
* [2025 (JD) (Arxiv) [GRAM] Generative Retrieval and Alignment Model - A New Paradigm for E-commerce Retrieval](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/LLM_Matching/2025%20%28JD%29%20%28Arxiv%29%20%5BGRAM%5D%20Generative%20Retrieval%20and%20Alignment%20Model%20-%20A%20New%20Paradigm%20for%20E-commerce%20Retrieval.pdf) <br />
* [2025 (Kuaishou) (AAAI) [Align3GR] Align3GR - Unified Multi-Level Alignment for LLM-based Generative Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/LLM_Matching/2025%20%28Kuaishou%29%20%28AAAI%29%20%5BAlign3GR%5D%20Align3GR%20-%20Unified%20Multi-Level%20Alignment%20for%20LLM-based%20Generative%20Recommendation.pdf) <br />
* [2025 (Kuaishou) (Arxiv) [LARM] LLM-Alignment Live-Streaming Recommendationpdf](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/LLM_Matching/2025%20%28Kuaishou%29%20%28Arxiv%29%20%5BLARM%5D%20LLM-Alignment%20Live-Streaming%20Recommendationpdf.pdf) <br />
* [2025 (Kuaishou) (Arxiv) [LEARN] LEARN - Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/LLM_Matching/2025%20%28Kuaishou%29%20%28Arxiv%29%20%5BLEARN%5D%20LEARN%20-%20Knowledge%20Adaptation%20from%20Large%20Language%20Model%20to%20Recommendation%20for%20Practical%20Industrial%20Application.pdf) <br />
* [2025 (Meta) (Arxiv) [DRAMA] DRAMA - Diverse Augmentation from Large Language Models to Smaller Dense Retrievers](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/LLM_Matching/2025%20%28Meta%29%20%28Arxiv%29%20%5BDRAMA%5D%20DRAMA%20-%20Diverse%20Augmentation%20from%20Large%20Language%20Models%20to%20Smaller%20Dense%20Retrievers.pdf) <br />
* [2025 (Meta) (Arxiv) [ROO] Request-Only Optimization for Recommendation Systems](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/LLM_Matching/2025%20%28Meta%29%20%28Arxiv%29%20%5BROO%5D%20Request-Only%20Optimization%20for%20Recommendation%20Systems.pdf) <br />
* [2025 (Pinterest) [PinRec] PinRec - Outcome-Conditioned, Multi-Token Generative Retrieval for Industry-Scale Recommendation Systems](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/LLM_Matching/2025%20%28Pinterest%29%20%5BPinRec%5D%20PinRec%20-%20Outcome-Conditioned%2C%20Multi-Token%20Generative%20Retrieval%20for%20Industry-Scale%20Recommendation%20Systems.pdf) <br />
* [2025 (Xiaohongshu) （KDD) [NoteLLM-2] NoteLLM-2 - Multimodal Large Representation Models for Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/02_Matching/LLM_Matching/2025%20%28Xiaohongshu%29%20%EF%BC%88KDD%29%20%5BNoteLLM-2%5D%20NoteLLM-2%20-%20Multimodal%20Large%20Representation%20Models%20for%20Recommendation.pdf) <br />

## 03_Pre-ranking
* [2020 (Alibaba) (DLP-KDD) [COLD] COLD - Towards the Next Generation of Pre-Ranking System](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/03_Pre-ranking/2020%20%28Alibaba%29%20%28DLP-KDD%29%20%5BCOLD%5D%20COLD%20-%20Towards%20the%20Next%20Generation%20of%20Pre-Ranking%20System.pdf) <br />
* [2022 (Huawei) (SIGIR) [RankFlow] RankFlow - JointOptimization ofMulti-Stage CascadeRanking SystemsasFlows](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/03_Pre-ranking/2022%20%28Huawei%29%20%28SIGIR%29%20%5BRankFlow%5D%20RankFlow%20-%20JointOptimization%20ofMulti-Stage%20CascadeRanking%20SystemsasFlows.pdf) <br />
* [2022 （Huawei) (CIKM) [IntTower] IntTower - the Next Generation of Two-Tower Model for Pre-Ranking System](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/03_Pre-ranking/2022%20%EF%BC%88Huawei%29%20%28CIKM%29%20%5BIntTower%5D%20IntTower%20-%20the%20Next%20Generation%20of%20Two-Tower%20Model%20for%20Pre-Ranking%20System.pdf) <br />
* [2023 (Alibaba) (CIKM) [COPR] COPR - Consistency-Oriented Pre-Ranking for Online Advertising](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/03_Pre-ranking/2023%20%28Alibaba%29%20%28CIKM%29%20%5BCOPR%5D%20COPR%20-%20Consistency-Oriented%20Pre-Ranking%20for%20Online%20Advertising.pdf) <br />
* [2023 (Alibaba) (KDD) [ASMOL] Rethinking the Role of Pre-ranking in Large-scale E-Commerce Searching System](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/03_Pre-ranking/2023%20%28Alibaba%29%20%28KDD%29%20%5BASMOL%5D%20Rethinking%20the%20Role%20of%20Pre-ranking%20in%20Large-scale%20E-Commerce%20Searching%20System.pdf) <br />
* [2025 (Tencent) (Arxiv) [HIT] HIT Model - A Hierarchical Interaction-Enhanced Two-Tower Model for Pre-Ranking Systems](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/03_Pre-ranking/2025%20%28Tencent%29%20%28Arxiv%29%20%5BHIT%5D%20HIT%20Model%20-%20A%20Hierarchical%20Interaction-Enhanced%20Two-Tower%20Model%20for%20Pre-Ranking%20Systems.pdf) <br />

## 04_Ranking
* [2014 (ADKDD) (Facebook) Practical Lessons from Predicting Clicks on Ads at Facebook](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2014%20%28ADKDD%29%20%28Facebook%29%20Practical%20Lessons%20from%20Predicting%20Clicks%20on%20Ads%20at%20Facebook.pdf) <br />
* [2016 (Google) (DLRS) **[Wide & Deep] Wide & Deep Learning for Recommender Systems](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2016%20%28Google%29%20%28DLRS%29%20%2A%2A%5BWide%20%26%20Deep%5D%20Wide%20%26%20Deep%20Learning%20for%20Recommender%20Systems.pdf) <br />
* [2016 (Google) (RecSys) **[Youtube DNN] Deep Neural Networks for YouTube Recommendations](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2016%20%28Google%29%20%28RecSys%29%20%2A%2A%5BYoutube%20DNN%5D%20Deep%20Neural%20Networks%20for%20YouTube%20Recommendations.pdf) <br />
* [2018 (Alibaba) (KDD) **[DIN] Deep Interest Network for Click-Through Rate Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2018%20%28Alibaba%29%20%28KDD%29%20%2A%2A%5BDIN%5D%20Deep%20Interest%20Network%20for%20Click-Through%20Rate%20Prediction.pdf) <br />
* [2019 (Alibaba) (AAAI) **[DIEN] Deep Interest Evolution Network for Click-Through Rate Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2019%20%28Alibaba%29%20%28AAAI%29%20%2A%2A%5BDIEN%5D%20Deep%20Interest%20Evolution%20Network%20for%20Click-Through%20Rate%20Prediction.pdf) <br />
* [2019 (Alibaba) (KDD) [MIMN] Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2019%20%28Alibaba%29%20%28KDD%29%20%5BMIMN%5D%20Practice%20on%20Long%20Sequential%20User%20Behavior%20Modeling%20for%20Click-Through%20Rate%20Prediction.pdf) <br />
* [2019 (CIKM) ** [AutoInt] AutoInt -Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2019%20%28CIKM%29%20%2A%2A%20%5BAutoInt%5D%20AutoInt%20-Automatic%20Feature%20Interaction%20Learning%20via%20Self-Attentive%20Neural%20Networks.pdf) <br />
* [2019 (Facebook) (Arxiv) [DLRM] (Facebook) Deep Learning Recommendation Model for Personalization and Recommendation Systems, Facebook](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2019%20%28Facebook%29%20%28Arxiv%29%20%5BDLRM%5D%20%28Facebook%29%20Deep%20Learning%20Recommendation%20Model%20for%20Personalization%20and%20Recommendation%20Systems%2C%20Facebook.pdf) <br />
* [2019 (Google) (Recsys) ** [Youtube Multi-task] Recommending what video to watch next - a multitask ranking system](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2019%20%28Google%29%20%28Recsys%29%20%2A%2A%20%5BYoutube%20Multi-task%5D%20Recommending%20what%20video%20to%20watch%20next%20-%20a%20multitask%20ranking%20system.pdf) <br />
* [2019 (Google) (WWW) Towards Neural Mixture Recommender for Long Range Dependent User Sequences](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2019%20%28Google%29%20%28WWW%29%20Towards%20Neural%20Mixture%20Recommender%20for%20Long%20Range%20Dependent%20User%20Sequences.pdf) <br />
* [2020 (Alibaba) (Arxiv) ** [SIM] Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2020%20%28Alibaba%29%20%28Arxiv%29%20%2A%2A%20%5BSIM%5D%20Search-based%20User%20Interest%20Modeling%20with%20Lifelong%20Sequential%20Behavior%20Data%20for%20Click-Through%20Rate%20Prediction.pdf) <br />
* [2020 (Alibaba) (NIPS) Neuron-level Structured Pruning using Polarization Regularizer](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2020%20%28Alibaba%29%20%28NIPS%29%20Neuron-level%20Structured%20Pruning%20using%20Polarization%20Regularizer.pdf) <br />
* [2020 (ICLR) Reformer - The Efficient Transformer ](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2020%20%28ICLR%29%20Reformer%20-%20The%20Efficient%20Transformer%20.pdf) <br />
* [2020 (JD) (CIKM) **[DMT] Deep Multifaceted Transformers for Multi-objective Ranking in Large-Scale E-commerce Recommender Systems](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2020%20%28JD%29%20%28CIKM%29%20%2A%2A%5BDMT%5D%20Deep%20Multifaceted%20Transformers%20for%20Multi-objective%20Ranking%20in%20Large-Scale%20E-commerce%20Recommender%20Systems.pdf) <br />
* [2020 (SIGIR) [UBR4CTR] User Behavior Retrieval for Click-Through Rate Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2020%20%28SIGIR%29%20%5BUBR4CTR%5D%20User%20Behavior%20Retrieval%20for%20Click-Through%20Rate%20Prediction.pdf) <br />
* [2020 (Tencent) (Recsys) **  [PLE] Progressive Layered Extraction (PLE) - A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2020%20%28Tencent%29%20%28Recsys%29%20%2A%2A%20%20%5BPLE%5D%20Progressive%20Layered%20Extraction%20%28PLE%29%20-%20A%20Novel%20Multi-Task%20Learning%20%28MTL%29%20Model%20for%20Personalized%20Recommendations.pdf) <br />
* [2021 (Alibaba) (Arxiv) [ETA] End-to-End User Behavior Retrieval in Click-Through Rate Prediction Model](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2021%20%28Alibaba%29%20%28Arxiv%29%20%5BETA%5D%20End-to-End%20User%20Behavior%20Retrieval%20in%20Click-Through%20Rate%20Prediction%20Model.pdf) <br />
* [2021 (Alibaba) (CIKM) * [ZEUS] Self-Supervised Learning on Users’ Spontaneous Behaviors for Multi-Scenario Ranking in E-commerce](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2021%20%28Alibaba%29%20%28CIKM%29%20%2A%20%5BZEUS%5D%20Self-Supervised%20Learning%20on%20Users%E2%80%99%20Spontaneous%20Behaviors%20for%20Multi-Scenario%20Ranking%20in%20E-commerce.pdf) <br />
* [2021 (Alibaba) (CIKM) [STAR] One Model to Serve All - Star Topology Adaptive Recommender for Multi-Domain CTR Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2021%20%28Alibaba%29%20%28CIKM%29%20%5BSTAR%5D%20One%20Model%20to%20Serve%20All%20-%20Star%20Topology%20Adaptive%20Recommender%20for%20Multi-Domain%20CTR%20Prediction.pdf) <br />
* [2021 (Google) (WWW) * [DCN V2] DCN V2 - Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2021%20%28Google%29%20%28WWW%29%20%2A%20%5BDCN%20V2%5D%20DCN%20V2%20-%20Improved%20Deep%20%26%20Cross%20Network%20and%20Practical%20Lessons%20for%20Web-scale%20Learning%20to%20Rank%20Systems.pdf) <br />
* [2022 (Alibaba) (Arxiv) ** [ETA] Efficient Long Sequential User Data Modeling for Click-Through Rate Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2022%20%28Alibaba%29%20%28Arxiv%29%20%2A%2A%20%5BETA%5D%20Efficient%20Long%20Sequential%20User%20Data%20Modeling%20for%20Click-Through%20Rate%20Prediction.pdf) <br />
* [2022 (Alibaba) (WSDM) Modeling Users’ Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2022%20%28Alibaba%29%20%28WSDM%29%20Modeling%20Users%E2%80%99%20Contextualized%20Page-wise%20Feedback%20for%20Click-Through%20Rate%20Prediction%20in%20E-commerce%20Search.pdf) <br />
* [2022 (Meituan) (CIKM) [SDIM] Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2022%20%28Meituan%29%20%28CIKM%29%20%5BSDIM%5D%20Sampling%20Is%20All%20You%20Need%20on%20Modeling%20Long-Term%20User%20Behaviors%20for%20CTR%20Prediction.pdf) <br />
* [2022 (Meta) ** (Arxiv) DHEN - A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2022%20%28Meta%29%20%2A%2A%20%28Arxiv%29%20DHEN%20-%20A%20Deep%20and%20Hierarchical%20Ensemble%20Network%20for%20Large-Scale%20Click-Through%20Rate%20Prediction.pdf) <br />
* [2023 (Alibaba) (Arxiv) [ESLM] Entire Space Learning Framework - Unbias Conversion Rate Prediction in Full Stages of Recommender System](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2023%20%28Alibaba%29%20%28Arxiv%29%20%5BESLM%5D%20Entire%20Space%20Learning%20Framework%20-%20Unbias%20Conversion%20Rate%20Prediction%20in%20Full%20Stages%20of%20Recommender%20System.pdf) <br />
* [2023 (Google) (Arxiv) On the Factory Floor - ML Engineering for Industrial-Scale Ads Recommendation Models](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2023%20%28Google%29%20%28Arxiv%29%20On%20the%20Factory%20Floor%20-%20ML%20Engineering%20for%20Industrial-Scale%20Ads%20Recommendation%20Models.pdf) <br />
* [2023 (Google) ** (Arxiv) [Hiformer] Hiformer - Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2023%20%28Google%29%20%2A%2A%20%28Arxiv%29%20%5BHiformer%5D%20Hiformer%20-%20Heterogeneous%20Feature%20Interactions%20Learning%20with%20Transformers%20for%20Recommender%20Systems.pdf) <br />
* [2023 (Kuaishou) (Arixiv) [TWIN] TWIN - TWo-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction at Kuaishou](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2023%20%28Kuaishou%29%20%28Arixiv%29%20%5BTWIN%5D%20TWIN%20-%20TWo-stage%20Interest%20Network%20for%20Lifelong%20User%20Behavior%20Modeling%20in%20CTR%20Prediction%20at%20Kuaishou.pdf) <br />
* [2023 (Kuaishou) (CIKM) [QIN] Query-dominant User Interest Network for Large-Scale Search Ranking](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2023%20%28Kuaishou%29%20%28CIKM%29%20%5BQIN%5D%20Query-dominant%20User%20Interest%20Network%20for%20Large-Scale%20Search%20Ranking.pdf) <br />
* [2023 (Kuaishou) (KDD) [PEPNet] PEPNet - Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2023%20%28Kuaishou%29%20%28KDD%29%20%5BPEPNet%5D%20PEPNet%20-%20Parameter%20and%20Embedding%20Personalized%20Network%20for%20Infusing%20with%20Personalized%20Prior%20Information.pdf) <br />
* [2024 (Kuaishou) (CIKM) [TWINv2] TWIN V2 - Scaling Ultra-Long User Behavior Sequence Modeling for Enhanced CTR Prediction at Kuaishou](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2024%20%28Kuaishou%29%20%28CIKM%29%20%5BTWINv2%5D%20TWIN%20V2%20-%20Scaling%20Ultra-Long%20User%20Behavior%20Sequence%20Modeling%20for%20Enhanced%20CTR%20Prediction%20at%20Kuaishou.pdf) <br />
* [2024 (Meta) (Arxiv) ** [GR] Actions Speak Louder than Words - Trillion-Parameter Sequential Transducers for Generative Recommendations](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2024%20%28Meta%29%20%28Arxiv%29%20%2A%2A%20%5BGR%5D%20Actions%20Speak%20Louder%20than%20Words%20-%20Trillion-Parameter%20Sequential%20Transducers%20for%20Generative%20Recommendations.pdf) <br />
* [2024 (Meta) ** (PMLR) [Wukong] Wukong - Towards a Scaling Law for Large-Scale Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2024%20%28Meta%29%20%2A%2A%20%28PMLR%29%20%5BWukong%5D%20Wukong%20-%20Towards%20a%20Scaling%20Law%20for%20Large-Scale%20Recommendation.pdf) <br />
* [2024 (Tencent) (KDD) [LCN] Cross-Domain LifeLong Sequential Modeling for Online Click-Through Rate Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2024%20%28Tencent%29%20%28KDD%29%20%5BLCN%5D%20Cross-Domain%20LifeLong%20Sequential%20Modeling%20for%20Online%20Click-Through%20Rate%20Prediction.pdf) <br />
* [2025 (Alibaba) (Arxiv) [MUSE] MUSE - A Simple Yet Effective Multimodal Search-Based Framework for Lifelong User Interest Modeling](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2025%20%28Alibaba%29%20%28Arxiv%29%20%5BMUSE%5D%20MUSE%20-%20A%20Simple%20Yet%20Effective%20Multimodal%20Search-Based%20Framework%20for%20Lifelong%20User%20Interest%20Modeling.pdf) <br />
* [2025 (Bytedance) ** (Arxiv) [LONGER] LONGER - Scaling Up Long Sequence Modeling in Industrial Recommenders](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2025%20%28Bytedance%29%20%2A%2A%20%28Arxiv%29%20%5BLONGER%5D%20LONGER%20-%20Scaling%20Up%20Long%20Sequence%20Modeling%20in%20Industrial%20Recommenders.pdf) <br />
* [2025 (Bytedance) ** (Arxiv) [RankMixer] RankMixer - Scaling Up Ranking Models in Industrial Recommenders](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2025%20%28Bytedance%29%20%2A%2A%20%28Arxiv%29%20%5BRankMixer%5D%20RankMixer%20-%20Scaling%20Up%20Ranking%20Models%20in%20Industrial%20Recommenders.pdf) <br />
* [2025 (Kuaishou) (KDD) [HiT-LBM] Hierarchical Tree Search-based User Lifelong Behavior Modeling on Large Language Model](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2025%20%28Kuaishou%29%20%28KDD%29%20%5BHiT-LBM%5D%20Hierarchical%20Tree%20Search-based%20User%20Lifelong%20Behavior%20Modeling%20on%20Large%20Language%20Model.pdf) <br />
* [2025 (Meta) (Arxiv) [VISTA] Massive Memorization with Hundreds of Trillions of Parameters for Sequential Transducer Generative Recommenders](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2025%20%28Meta%29%20%28Arxiv%29%20%5BVISTA%5D%20Massive%20Memorization%20with%20Hundreds%20of%20Trillions%20of%20Parameters%20for%20Sequential%20Transducer%20Generative%20Recommenders.pdf) <br />
* [2025 (Meta) (KDD) DV365 - Extremely Long User History Modeling at Instagram](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2025%20%28Meta%29%20%28KDD%29%20DV365%20-%20Extremely%20Long%20User%20History%20Modeling%20at%20Instagram.pdf) <br />
* [2025 (Pinterest) (Arxiv) [TransActV2]TransAct V2 - Lifelong User Action Sequence Modeling on Pinterest Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2025%20%28Pinterest%29%20%28Arxiv%29%20%5BTransActV2%5DTransAct%20V2%20-%20Lifelong%20User%20Action%20Sequence%20Modeling%20on%20Pinterest%20Recommendation.pdf) <br />
* [2025 （Bytedance) ** (Arxiv) [STCA] Make It Long, Keep It Fast - End-to-End 10k-Sequence Modeling at Billion Scale on Douyin](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2025%20%EF%BC%88Bytedance%29%20%2A%2A%20%28Arxiv%29%20%5BSTCA%5D%20Make%20It%20Long%2C%20Keep%20It%20Fast%20-%20End-to-End%2010k-Sequence%20Modeling%20at%20Billion%20Scale%20on%20Douyin.pdf) <br />
* [2026 (Meta) (KDD) [Lattice] Meta Lattice - Model Space Redesign for Cost-Effective Industry-Scale Ads Recommendations](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/2026%20%28Meta%29%20%28KDD%29%20%5BLattice%5D%20Meta%20Lattice%20-%20Model%20Space%20Redesign%20for%20Cost-Effective%20Industry-Scale%20Ads%20Recommendations.pdf) <br />

#### Activation-Function
* [2020(Arxiv)  [GLU] GLU Variants Improve Transformer](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Activation-Function/2020%28Arxiv%29%20%20%5BGLU%5D%20GLU%20Variants%20Improve%20Transformer.pdf) <br />

#### Calibration
* [2014 (ADKDD) (Facebook) Practical Lessons from Predicting Clicks on Ads at Facebook](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Calibration/2014%20%28ADKDD%29%20%28Facebook%29%20Practical%20Lessons%20from%20Predicting%20Clicks%20on%20Ads%20at%20Facebook.pdf) <br />
* [2014 (TIST) Simple and scalable response prediction for display advertising](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Calibration/2014%20%28TIST%29%20Simple%20and%20scalable%20response%20prediction%20for%20display%20advertising.pdf) <br />
* [2023 Classifier Calibration with ROC-Regularized Isotonic Regression](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Calibration/2023%20Classifier%20Calibration%20with%20ROC-Regularized%20Isotonic%20Regression.pdf) <br />

#### Classic
* [2016 (ICLR) [GRU4Rec] Session-based Recommendations with Recurrent Neural Networks](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Classic/2016%20%28ICLR%29%20%5BGRU4Rec%5D%20Session-based%20Recommendations%20with%20Recurrent%20Neural%20Networks.pdf) <br />
* [2017 (Amazon) (IEEE) Two decades of recommender systems at Amazon.com](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Classic/2017%20%28Amazon%29%20%28IEEE%29%20Two%20decades%20of%20recommender%20systems%20at%20Amazon.com.pdf) <br />

#### DNN
* [2019 (KDD) (Airbnb) Applying Deep Learning To Airbnb Search](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/DNN/2019%20%28KDD%29%20%28Airbnb%29%20Applying%20Deep%20Learning%20To%20Airbnb%20Search.pdf) <br />
* [2020 (Airbnb) (KDD) Improving Deep Learning For Airbnb Search](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/DNN/2020%20%28Airbnb%29%20%28KDD%29%20Improving%20Deep%20Learning%20For%20Airbnb%20Search.pdf) <br />

#### Delayed-Feedback-Problem
* [2008 (KDD) Learning Classifiers from Only Positive and Unlabeled Data](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Delayed-Feedback-Problem/2008%20%28KDD%29%20Learning%20Classifiers%20from%20Only%20Positive%20and%20Unlabeled%20Data.pdf) <br />
* [2014 (Criteo) (KDD) [DFM] Modeling Delayed Feedback in Display Advertising](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Delayed-Feedback-Problem/2014%20%28Criteo%29%20%28KDD%29%20%5BDFM%5D%20Modeling%20Delayed%20Feedback%20in%20Display%20Advertising.pdf) <br />
* [2018 (Arxiv) [NoDeF] A Nonparametric Delayed Feedback Model for Conversion Rate Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Delayed-Feedback-Problem/2018%20%28Arxiv%29%20%5BNoDeF%5D%20A%20Nonparametric%20Delayed%20Feedback%20Model%20for%20Conversion%20Rate%20Prediction.pdf) <br />
* [2019 (Twitter) (RecSys) Addressing Delayed Feedback for Continuous Training with Neural Networks in CTR prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Delayed-Feedback-Problem/2019%20%28Twitter%29%20%28RecSys%29%20Addressing%20Delayed%20Feedback%20for%20Continuous%20Training%20with%20Neural%20Networks%20in%20CTR%20prediction.pdf) <br />
* [2020 (AdKDD) Delayed Feedback Model with Negative Binomial Regression for Multiple Conversions](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Delayed-Feedback-Problem/2020%20%28AdKDD%29%20Delayed%20Feedback%20Model%20with%20Negative%20Binomial%20Regression%20for%20Multiple%20Conversions.pdf) <br />
* [2020 (JD) (IJCAI) [TS-DL] An Attention-based Model for Conversion Rate Prediction with Delayed Feedback via Post-click Calibration](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Delayed-Feedback-Problem/2020%20%28JD%29%20%28IJCAI%29%20%5BTS-DL%5D%20An%20Attention-based%20Model%20for%20Conversion%20Rate%20Prediction%20with%20Delayed%20Feedback%20via%20Post-click%20Calibration.pdf) <br />
* [2020 (SIGIR) [DLA-DF] Dual Learning Algorithm for Delayed Conversions](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Delayed-Feedback-Problem/2020%20%28SIGIR%29%20%5BDLA-DF%5D%20Dual%20Learning%20Algorithm%20for%20Delayed%20Conversions.pdf) <br />
* [2020 (WWW) [FSIW] A Feedback Shift Correction in Predicting Conversion Rates under Delayed Feedback](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Delayed-Feedback-Problem/2020%20%28WWW%29%20%5BFSIW%5D%20A%20Feedback%20Shift%20Correction%20in%20Predicting%20Conversion%20Rates%20under%20Delayed%20Feedback.pdf) <br />
* [2021 (Alibaba) (AAAI) [ES-DFM] Capturing Delayed Feedback in Conversion Rate Prediction via Elapsed-Time Sampling](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Delayed-Feedback-Problem/2021%20%28Alibaba%29%20%28AAAI%29%20%5BES-DFM%5D%20Capturing%20Delayed%20Feedback%20in%20Conversion%20Rate%20Prediction%20via%20Elapsed-Time%20Sampling.pdf) <br />
* [2021 (Alibaba) (AAAI) [ESDF] Delayed Feedback Modeling for the Entire Space Conversion Rate Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Delayed-Feedback-Problem/2021%20%28Alibaba%29%20%28AAAI%29%20%5BESDF%5D%20Delayed%20Feedback%20Modeling%20for%20the%20Entire%20Space%20Conversion%20Rate%20Prediction.pdf) <br />
* [2021 (Alibaba) (Arxiv) [Defer] Real Negatives Matter - Continuous Training with Real Negatives for Delayed Feedback Modeling](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Delayed-Feedback-Problem/2021%20%28Alibaba%29%20%28Arxiv%29%20%5BDefer%5D%20Real%20Negatives%20Matter%20-%20Continuous%20Training%20with%20Real%20Negatives%20for%20Delayed%20Feedback%20Modeling.pdf) <br />
* [2021 (Google) (Arxiv) Handling many conversions per click in modeling delayed feedback](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Delayed-Feedback-Problem/2021%20%28Google%29%20%28Arxiv%29%20Handling%20many%20conversions%20per%20click%20in%20modeling%20delayed%20feedback.pdf) <br />
* [2021 (Tencent) (SIGIR) Counterfactual Reward Modification for Streaming Recommendation with Delayed Feedback](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Delayed-Feedback-Problem/2021%20%28Tencent%29%20%28SIGIR%29%20Counterfactual%20Reward%20Modification%20for%20Streaming%20Recommendation%20with%20Delayed%20Feedback.pdf) <br />
* [2022 (Alibaba) (WWW) [DEFUSE] Asymptotically Unbiased Estimation for Delayed Feedback Modeling via Label Correction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Delayed-Feedback-Problem/2022%20%28Alibaba%29%20%28WWW%29%20%5BDEFUSE%5D%20Asymptotically%20Unbiased%20Estimation%20for%20Delayed%20Feedback%20Modeling%20via%20Label%20Correction.pdf) <br />

#### Distill
* [2020 (Alibaba) (KDD) *[Privileged Features Distillation] Privileged Features Distillation at Taobao Recommendations](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Distill/2020%20%28Alibaba%29%20%28KDD%29%20%2A%5BPrivileged%20Features%20Distillation%5D%20Privileged%20Features%20Distillation%20at%20Taobao%20Recommendations.pdf) <br />
* [2024 (Google) Self-Auxiliary Distillation for Sample Efficient Learning in Google-Scale Recommenders](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Distill/2024%20%28Google%29%20Self-Auxiliary%20Distillation%20for%20Sample%20Efficient%20Learning%20in%20Google-Scale%20Recommenders.pdf) <br />
* [2025 (Bytedance) (KDD) [HA-PFD] Hardness-aware Privileged Features Distillation with Latent Alignment for CVR Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Distill/2025%20%28Bytedance%29%20%28KDD%29%20%5BHA-PFD%5D%20Hardness-aware%20Privileged%20Features%20Distillation%20with%20Latent%20Alignment%20for%20CVR%20Prediction.pdf) <br />
* [2025 (Kuaishou) [MIKD] Mutual Information-aware Knowledge Distillation for Short Video Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Distill/2025%20%28Kuaishou%29%20%5BMIKD%5D%20Mutual%20Information-aware%20Knowledge%20Distillation%20for%20Short%20Video%20Recommendation.pdf) <br />
* [2026 （Bytedance) (Arxiv) [Rec-Distill] Rec-Distill - An Industrial Distillation Pipeline for Large-Scale Recommendation Models](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Distill/2026%20%EF%BC%88Bytedance%29%20%28Arxiv%29%20%5BRec-Distill%5D%20Rec-Distill%20-%20An%20Industrial%20Distillation%20Pipeline%20for%20Large-Scale%20Recommendation%20Models.pdf) <br />

#### Experiment
* [2010 (Google) Overlapping Experiment Infrastructure - More, Better, Faster Experimentation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Experiment/2010%20%28Google%29%20Overlapping%20Experiment%20Infrastructure%20-%20More%2C%20Better%2C%20Faster%20Experimentation.pdf) <br />
* [2019 (Alibaba) (KDD) OptimizedCost perClickin TaobaoDisplayAdvertising](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Experiment/2019%20%28Alibaba%29%20%28KDD%29%20OptimizedCost%20perClickin%20TaobaoDisplayAdvertising.pdf) <br />
* [2022 (Google) (KDD) Scale Calibration of Deep Ranking Models](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Experiment/2022%20%28Google%29%20%28KDD%29%20Scale%20Calibration%20of%20Deep%20Ranking%20Models.pdf) <br />

#### Feature-Crossing
* [2010 (ICDM) [FM] Factorization machines](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2010%20%28ICDM%29%20%5BFM%5D%20Factorization%20machines.pdf) <br />
* [2013 (Google) (KDD) [LR] Ad Click Prediction - a View from the Trenches](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2013%20%28Google%29%20%28KDD%29%20%5BLR%5D%20Ad%20Click%20Prediction%20-%20a%20View%20from%20the%20Trenches.pdf) <br />
* [2016 (Arxiv) [PNN] Product-based Neural Networks for User Response Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2016%20%28Arxiv%29%20%5BPNN%5D%20Product-based%20Neural%20Networks%20for%20User%20Response%20Prediction.pdf) <br />
* [2016 (Criteo) (Recsys) [FFM] Field-aware Factorization Machines for CTR Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2016%20%28Criteo%29%20%28Recsys%29%20%5BFFM%5D%20Field-aware%20Factorization%20Machines%20for%20CTR%20Prediction.pdf) <br />
* [2016 (ECIR) [FNN] Deep Learning over Multi-field Categorical Data – A Case Study on User Response Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2016%20%28ECIR%29%20%5BFNN%5D%20Deep%20Learning%20over%20Multi-field%20Categorical%20Data%20%E2%80%93%20A%20Case%20Study%20on%20User%20Response%20Prediction.pdf) <br />
* [2016 (KDD) [Deepintent] Deepintent - Learning attentions for online advertising with recurrent neural networks](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2016%20%28KDD%29%20%5BDeepintent%5D%20Deepintent%20-%20Learning%20attentions%20for%20online%20advertising%20with%20recurrent%20neural%20networks.pdf) <br />
* [2016 (Microsoft) (KDD) [Deep Crossing] Deep Crossing - Web-scale modeling without manually crafted combinatorial features](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2016%20%28Microsoft%29%20%28KDD%29%20%5BDeep%20Crossing%5D%20Deep%20Crossing%20-%20Web-scale%20modeling%20without%20manually%20crafted%20combinatorial%20features.pdf) <br />
* [2017 (Google) (ADKDD) [DCN] Deep & CrossNetwork for Ad Click Predictions](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2017%20%28Google%29%20%28ADKDD%29%20%5BDCN%5D%20Deep%20%26%20CrossNetwork%20for%20Ad%20Click%20Predictions.pdf) <br />
* [2017 (Huawei)  (IJCAI) [DeepFM] DeepFM - A Factorization-Machine based Neural Network for CTR Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2017%20%28Huawei%29%20%20%28IJCAI%29%20%5BDeepFM%5D%20DeepFM%20-%20A%20Factorization-Machine%20based%20Neural%20Network%20for%20CTR%20Prediction.pdf) <br />
* [2017 (IJCAI) [AFM] Attentional Factorization Machines Learning the Weight of Feature Interactions via Attention Networks](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2017%20%28IJCAI%29%20%5BAFM%5D%20Attentional%20Factorization%20Machines%20Learning%20the%20Weight%20of%20Feature%20Interactions%20via%20Attention%20Networks.pdf) <br />
* [2017 (SIGIR) [NFM] Neural Factorization Machines for Sparse Predictive Analytics](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2017%20%28SIGIR%29%20%5BNFM%5D%20Neural%20Factorization%20Machines%20for%20Sparse%20Predictive%20Analytics.pdf) <br />
* [2017 (WWW) [NCF] Neural Collaborative Filtering](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2017%20%28WWW%29%20%5BNCF%5D%20Neural%20Collaborative%20Filtering.pdf) <br />
* [2018 (Google) (WSDM) [Latent Cross] Latent Cross Making Use of Context in Recurrent Recommender Systems](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2018%20%28Google%29%20%28WSDM%29%20%5BLatent%20Cross%5D%20Latent%20Cross%20Making%20Use%20of%20Context%20in%20Recurrent%20Recommender%20Systems.pdf) <br />
* [2018 (KDD) [xDeepFM] xDeepFM - Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2018%20%28KDD%29%20%5BxDeepFM%5D%20xDeepFM%20-%20Combining%20Explicit%20and%20Implicit%20Feature%20Interactions%20for%20Recommender%20Systems.pdf) <br />
* [2018 (TOIS) [PNN] Product-Based Neural Networks for User Response Prediction over Multi-Field Categorical Data](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2018%20%28TOIS%29%20%5BPNN%5D%20Product-Based%20Neural%20Networks%20for%20User%20Response%20Prediction%20over%20Multi-Field%20Categorical%20Data.pdf) <br />
* [2019 (CIKM) ** [AutoInt] AutoInt - Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2019%20%28CIKM%29%20%2A%2A%20%5BAutoInt%5D%20AutoInt%20-%20Automatic%20Feature%20Interaction%20Learning%20via%20Self-Attentive%20Neural%20Networks.pdf) <br />
* [2019 (Huawei) (WWW) [FGCNN] Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2019%20%28Huawei%29%20%28WWW%29%20%5BFGCNN%5D%20Feature%20Generation%20by%20Convolutional%20Neural%20Network%20for%20Click-Through%20Rate%20Prediction.pdf) <br />
* [2019 (Sina) (Arxiv) [FAT-DeepFFM] FAT-DeepFFM - Field Attentive Deep Field-aware Factorization Machine](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2019%20%28Sina%29%20%28Arxiv%29%20%5BFAT-DeepFFM%5D%20FAT-DeepFFM%20-%20Field%20Attentive%20Deep%20Field-aware%20Factorization%20Machine.pdf) <br />
* [2019 (Tencent) (AAAI) [IFM] Interaction-aware Factorization Machines for Recommender Systems](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2019%20%28Tencent%29%20%28AAAI%29%20%5BIFM%5D%20Interaction-aware%20Factorization%20Machines%20for%20Recommender%20Systems.pdf) <br />
* [2020 (Baidu) (KDD) [CAN] Combo-Attention Network for Baidu Video Advertising](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2020%20%28Baidu%29%20%28KDD%29%20%5BCAN%5D%20Combo-Attention%20Network%20for%20Baidu%20Video%20Advertising.pdf) <br />
* [2021 (Google) (NIPS) [MLP-Mixer] MLP-Mixer - An all-MLP Architecture for Vision](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2021%20%28Google%29%20%28NIPS%29%20%5BMLP-Mixer%5D%20MLP-Mixer%20-%20An%20all-MLP%20Architecture%20for%20Vision.pdf) <br />
* [2021 (Google) (WWW) * [DCN V2] DCN V2 - Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2021%20%28Google%29%20%28WWW%29%20%2A%20%5BDCN%20V2%5D%20DCN%20V2%20-%20Improved%20Deep%20%26%20Cross%20Network%20and%20Practical%20Lessons%20for%20Web-scale%20Learning%20to%20Rank%20Systems.pdf) <br />
* [2022 (Alibaba) (WSDM) *  [CAN] CAN - Feature Co-Action Network for Click-Through Rate Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2022%20%28Alibaba%29%20%28WSDM%29%20%2A%20%20%5BCAN%5D%20CAN%20-%20Feature%20Co-Action%20Network%20for%20Click-Through%20Rate%20Prediction.pdf) <br />
* [2022 (Meta) ** (Arxiv) DHEN - A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2022%20%28Meta%29%20%2A%2A%20%28Arxiv%29%20DHEN%20-%20A%20Deep%20and%20Hierarchical%20Ensemble%20Network%20for%20Large-Scale%20Click-Through%20Rate%20Prediction.pdf) <br />
* [2023 (CIKM) * [GDCN] Towards Deeper, Lighter and Interpretable Cross Network for CTR Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2023%20%28CIKM%29%20%2A%20%5BGDCN%5D%20Towards%20Deeper%2C%20Lighter%20and%20Interpretable%20Cross%20Network%20for%20CTR%20Prediction.pdf) <br />
* [2023 (Google) ** (Arxiv) [Hiformer] Hiformer - Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2023%20%28Google%29%20%2A%2A%20%28Arxiv%29%20%5BHiformer%5D%20Hiformer%20-%20Heterogeneous%20Feature%20Interactions%20Learning%20with%20Transformers%20for%20Recommender%20Systems.pdf) <br />
* [2023 (Sina) (CIKM) [MemoNet] MemoNet - Memorizing All Cross Features’ Representations Efficiently via Multi-Hash Codebook Network for CTR Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2023%20%28Sina%29%20%28CIKM%29%20%5BMemoNet%5D%20MemoNet%20-%20Memorizing%20All%20Cross%20Features%E2%80%99%20Representations%20Efficiently%20via%20Multi-Hash%20Codebook%20Network%20for%20CTR%20Prediction.pdf) <br />
* [2024 (Google) (ICLR) From Sparse to Soft Mixtures of Experts](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2024%20%28Google%29%20%28ICLR%29%20From%20Sparse%20to%20Soft%20Mixtures%20of%20Experts.pdf) <br />
* [2024 (Meta) ** (PMLR) [Wukong] Wukong - Towards a Scaling Law for Large-Scale Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2024%20%28Meta%29%20%2A%2A%20%28PMLR%29%20%5BWukong%5D%20Wukong%20-%20Towards%20a%20Scaling%20Law%20for%20Large-Scale%20Recommendation.pdf) <br />
* [2024 （LinkedIn) (KDD) [RDCN] LiRank - Industrial Large Scale Ranking Models at LinkedIn](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2024%20%EF%BC%88LinkedIn%29%20%28KDD%29%20%5BRDCN%5D%20LiRank%20-%20Industrial%20Large%20Scale%20Ranking%20Models%20at%20LinkedIn.pdf) <br />
* [2025 (Alibaba) [HHFT] HHFT - Hierarchical Heterogeneous Feature Transformer for Recommendation Systems](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2025%20%28Alibaba%29%20%5BHHFT%5D%20HHFT%20-%20Hierarchical%20Heterogeneous%20Feature%20Transformer%20for%20Recommendation%20Systems.pdf) <br />
* [2025 (Bytedance) (Arxiv) [RankMixer] RankMixer - Scaling Up Ranking Models in Industrial Recommenders](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2025%20%28Bytedance%29%20%28Arxiv%29%20%5BRankMixer%5D%20RankMixer%20-%20Scaling%20Up%20Ranking%20Models%20in%20Industrial%20Recommenders.pdf) <br />
* [2025 (Bytedance) ** (Arxiv) [Pyramid Mixer] Pyramid Mixer - Multi-dimensional Multi-period Interest Modeling for Sequential Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2025%20%28Bytedance%29%20%2A%2A%20%28Arxiv%29%20%5BPyramid%20Mixer%5D%20Pyramid%20Mixer%20-%20Multi-dimensional%20Multi-period%20Interest%20Modeling%20for%20Sequential%20Recommendation.pdf) <br />
* [2025 (Bytedance) ** (Arxiv) [RankMixer] RankMixer - Scaling Up Ranking Models in Industrial Recommenders](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2025%20%28Bytedance%29%20%2A%2A%20%28Arxiv%29%20%5BRankMixer%5D%20RankMixer%20-%20Scaling%20Up%20Ranking%20Models%20in%20Industrial%20Recommenders.pdf) <br />
* [2025 (Bytedance) ** (CIKM) [RankMixer] RankMixer - Scaling Up Ranking Models in Industrial Recommenders](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2025%20%28Bytedance%29%20%2A%2A%20%28CIKM%29%20%5BRankMixer%5D%20RankMixer%20-%20Scaling%20Up%20Ranking%20Models%20in%20Industrial%20Recommenders.pdf) <br />
* [2025 (Google) ** (Arxiv) [Hiformer] Hiformer - Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2025%20%28Google%29%20%2A%2A%20%28Arxiv%29%20%5BHiformer%5D%20Hiformer%20-%20Heterogeneous%20Feature%20Interactions%20Learning%20with%20Transformers%20for%20Recommender%20Systems.pdf) <br />
* [2025 (Meta) (CIKM) [InterFormer] InterFormer - Effective Heterogeneous Interaction Learning for Click-Through Rate Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2025%20%28Meta%29%20%28CIKM%29%20%5BInterFormer%5D%20InterFormer%20-%20Effective%20Heterogeneous%20Interaction%20Learning%20for%20Click-Through%20Rate%20Prediction.pdf) <br />
* [2025 (Tencent) (Arxiv) [D-MoE] Enhancing CTR Prediction with De-correlated Expert Networks](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2025%20%28Tencent%29%20%28Arxiv%29%20%5BD-MoE%5D%20Enhancing%20CTR%20Prediction%20with%20De-correlated%20Expert%20Networks.pdf) <br />
* [2025 （Alibaba) [FAT] From Scaling to Structured Expressivity - Rethinking Transformers for CTR Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2025%20%EF%BC%88Alibaba%29%20%5BFAT%5D%20From%20Scaling%20to%20Structured%20Expressivity%20-%20Rethinking%20Transformers%20for%20CTR%20Prediction.pdf) <br />
* [2025 （Bytedance) (Arxiv) [OneTrans] OneTrans - Unified Feature Interaction and Sequence Modeling with One Transformer in Industrial Recommender](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2025%20%EF%BC%88Bytedance%29%20%28Arxiv%29%20%5BOneTrans%5D%20OneTrans%20-%20Unified%20Feature%20Interaction%20and%20Sequence%20Modeling%20with%20One%20Transformer%20in%20Industrial%20Recommender.pdf) <br />
* [2026 (Alibaba) (Arxiv) [EST] EST - Towards Efficient Scaling Laws in Click-Through Rate Prediction via Unified Modeling](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2026%20%28Alibaba%29%20%28Arxiv%29%20%5BEST%5D%20EST%20-%20Towards%20Efficient%20Scaling%20Laws%20in%20Click-Through%20Rate%20Prediction%20via%20Unified%20Modeling.pdf) <br />
* [2026 (Alibaba) (Arxiv) [MGDIN] Deferred is Better - A Framework for Multi-Granularity Deferred Interaction of Heterogeneous Features](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2026%20%28Alibaba%29%20%28Arxiv%29%20%5BMGDIN%5D%20Deferred%20is%20Better%20-%20A%20Framework%20for%20Multi-Granularity%20Deferred%20Interaction%20of%20Heterogeneous%20Features.pdf) <br />
* [2026 (Bytedance) (Arxiv) [MixFormer] MixFormer - Co-Scaling Up Dense and Sequence in Industrial Recommenders](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2026%20%28Bytedance%29%20%28Arxiv%29%20%5BMixFormer%5D%20MixFormer%20-%20Co-Scaling%20Up%20Dense%20and%20Sequence%20in%20Industrial%20Recommenders.pdf) <br />
* [2026 (Bytedance) (Arxiv) [TokenMixer-Large] TokenMixer-Large - Scaling Up Large Ranking Models in Industrial Recommenders](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2026%20%28Bytedance%29%20%28Arxiv%29%20%5BTokenMixer-Large%5D%20TokenMixer-Large%20-%20Scaling%20Up%20Large%20Ranking%20Models%20in%20Industrial%20Recommenders.pdf) <br />
* [2026 (Bytedance) (Arxiv) [UG-Sep] Compute Only Once - UG-Separation for Efficient Large Recommendation Models](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2026%20%28Bytedance%29%20%28Arxiv%29%20%5BUG-Sep%5D%20Compute%20Only%20Once%20-%20UG-Separation%20for%20Efficient%20Large%20Recommendation%20Models.pdf) <br />
* [2026 (Bytedance) (Arxiv) [Zenith] Zenith - Scaling up Ranking Models for Billion-scale Livestreaming Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2026%20%28Bytedance%29%20%28Arxiv%29%20%5BZenith%5D%20Zenith%20-%20Scaling%20up%20Ranking%20Models%20for%20Billion-scale%20Livestreaming%20Recommendation.pdf) <br />
* [2026 (Coupon) [Arxiv] On the Practice of Scaling Search Conversion Rate Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2026%20%28Coupon%29%20%5BArxiv%5D%20On%20the%20Practice%20of%20Scaling%20Search%20Conversion%20Rate%20Prediction.pdf) <br />
* [2026 (Kuaishou) (Arxiv) [UniMixer] UniMixer - A Unified Architecture for Scaling Laws in Recommendation Systems](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2026%20%28Kuaishou%29%20%28Arxiv%29%20%5BUniMixer%5D%20UniMixer%20-%20A%20Unified%20Architecture%20for%20Scaling%20Laws%20in%20Recommendation%20Systems.pdf) <br />
* [2026 (Tencent) (Arxiv) [RankUp] RankUp - Towards High-rank Representations for Large Scale Advertising Recommender Systems](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2026%20%28Tencent%29%20%28Arxiv%29%20%5BRankUp%5D%20RankUp%20-%20Towards%20High-rank%20Representations%20for%20Large%20Scale%20Advertising%20Recommender%20Systems.pdf) <br />
* [2026 （Bytedance) (Arxiv) [Rec-Distill] Rec-Distill - An Industrial Distillation Pipeline for Large-Scale Recommendation Models](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2026%20%EF%BC%88Bytedance%29%20%28Arxiv%29%20%5BRec-Distill%5D%20Rec-Distill%20-%20An%20Industrial%20Distillation%20Pipeline%20for%20Large-Scale%20Recommendation%20Models.pdf) <br />
* [2026 （Tencent) (Arxiv) [TokenFormer] TokenFormer - Unify the Multi-Field and Sequential Recommendation Worlds](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature-Crossing/2026%20%EF%BC%88Tencent%29%20%28Arxiv%29%20%5BTokenFormer%5D%20TokenFormer%20-%20Unify%20the%20Multi-Field%20and%20Sequential%20Recommendation%20Worlds.pdf) <br />

#### Feature_Importance
* [2022 (Kuaishou) (Arxiv) [LPFS] LPFS - Learnable Polarizing Feature Selection for Click-Through Rate Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature_Importance/2022%20%28Kuaishou%29%20%28Arxiv%29%20%5BLPFS%5D%20LPFS%20-%20Learnable%20Polarizing%20Feature%20Selection%20for%20Click-Through%20Rate%20Prediction.pdf) <br />
* [2024 (Huawei)(KDD) ERASE - Benchmarking Feature Selection Methods for Deep Recommender Systems](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Feature_Importance/2024%20%28Huawei%29%28KDD%29%20ERASE%20-%20Benchmarking%20Feature%20Selection%20Methods%20for%20Deep%20Recommender%20Systems.pdf) <br />

#### Gating
* [2014 (TASLP) * [LHUC] Learning Hidden Unit Contributions for Unsupervised Acoustic Model Adaptation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Gating/2014%20%28TASLP%29%20%2A%20%5BLHUC%5D%20Learning%20Hidden%20Unit%20Contributions%20for%20Unsupervised%20Acoustic%20Model%20Adaptation.pdf) <br />
* [2018 (CVPR) * [SENet] Squeeze-and-Excitation Networks](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Gating/2018%20%28CVPR%29%20%2A%20%5BSENet%5D%20Squeeze-and-Excitation%20Networks.pdf) <br />
* [2019 (Sina) (Recsys) [FiBiNET] FiBiNET - combining feature importance and bilinear feature interaction for click-through rate prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Gating/2019%20%28Sina%29%20%28Recsys%29%20%5BFiBiNET%5D%20FiBiNET%20-%20combining%20feature%20importance%20and%20bilinear%20feature%20interaction%20for%20click-through%20rate%20prediction.pdf) <br />
* [2020 (Sina) (Arxiv) [GateNet] GateNet - Gating-Enhanced Deep Network for Click-Through Rate Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Gating/2020%20%28Sina%29%20%28Arxiv%29%20%5BGateNet%5D%20GateNet%20-%20Gating-Enhanced%20Deep%20Network%20for%20Click-Through%20Rate%20Prediction.pdf) <br />
* [2021 (Sina) (Arxiv) [ContextNet] ContextNet - A Click-Through Rate Prediction Framework Using Contextual information to Refine Feature Embedding](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Gating/2021%20%28Sina%29%20%28Arxiv%29%20%5BContextNet%5D%20ContextNet%20-%20A%20Click-Through%20Rate%20Prediction%20Framework%20Using%20Contextual%20information%20to%20Refine%20Feature%20Embedding.pdf) <br />
* [2021 (Sina) （DLP-KDD) [MaskNet] MaskNet - Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Gating/2021%20%28Sina%29%20%EF%BC%88DLP-KDD%29%20%5BMaskNet%5D%20MaskNet%20-%20Introducing%20Feature-Wise%20Multiplication%20to%20CTR%20Ranking%20Models%20by%20Instance-Guided%20Mask.pdf) <br />
* [2023 (Kuaishou) (KDD) [PEPNet] PEPNet - Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Gating/2023%20%28Kuaishou%29%20%28KDD%29%20%5BPEPNet%5D%20PEPNet%20-%20Parameter%20and%20Embedding%20Personalized%20Network%20for%20Infusing%20with%20Personalized%20Prior%20Information.pdf) <br />
* [2023 (Sina) (CIKM) [FiBiNet++] FiBiNet++ - Reducing Model Size by Low Rank Feature Interaction Layer for CTR Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Gating/2023%20%28Sina%29%20%28CIKM%29%20%5BFiBiNet%2B%2B%5D%20FiBiNet%2B%2B%20-%20Reducing%20Model%20Size%20by%20Low%20Rank%20Feature%20Interaction%20Layer%20for%20CTR%20Prediction.pdf) <br />
* [2025 (Bytedance) [ADS] Adaptive Domain Scaling for Personalized Sequential Modeling in Recommenders](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Gating/2025%20%28Bytedance%29%20%5BADS%5D%20Adaptive%20Domain%20Scaling%20for%20Personalized%20Sequential%20Modeling%20in%20Recommenders.pdf) <br />

#### LLM_Ranking
* [2019 (CIKM)  [AutoInt] AutoInt -Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2019%20%28CIKM%29%20%20%5BAutoInt%5D%20AutoInt%20-Automatic%20Feature%20Interaction%20Learning%20via%20Self-Attentive%20Neural%20Networks.pdf) <br />
* [2020 (Arxiv) Scaling Laws for Neural Language Models](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2020%20%28Arxiv%29%20Scaling%20Laws%20for%20Neural%20Language%20Models.pdf) <br />
* [2021 (Baidu) (KDD) Pre-trained Language Model based Ranking in Baidu Search](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2021%20%28Baidu%29%20%28KDD%29%20Pre-trained%20Language%20Model%20based%20Ranking%20in%20Baidu%20Search.pdf) <br />
* [2023 (Arxiv) [E4SRec] E4SRec - An Elegant Effective Efficient Extensible Solution of Large Language Models for Sequential Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2023%20%28Arxiv%29%20%5BE4SRec%5D%20E4SRec%20-%20An%20Elegant%20Effective%20Efficient%20Extensible%20Solution%20of%20Large%20Language%20Models%20for%20Sequential%20Recommendation.pdf) <br />
* [2024 (Alibaba) (Arxiv) [BAHE] Breaking the Length Barrier - LLM-Enhanced CTR Prediction in Long Textual User Behaviors](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2024%20%28Alibaba%29%20%28Arxiv%29%20%5BBAHE%5D%20Breaking%20the%20Length%20Barrier%20-%20LLM-Enhanced%20CTR%20Prediction%20in%20Long%20Textual%20User%20Behaviors.pdf) <br />
* [2024 (Bytedance) (Arxiv) [HLLM] HLLM - Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User Modeling](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2024%20%28Bytedance%29%20%28Arxiv%29%20%5BHLLM%5D%20HLLM%20-%20Enhancing%20Sequential%20Recommendations%20via%20Hierarchical%20Large%20Language%20Models%20for%20Item%20and%20User%20Modeling.pdf) <br />
* [2024 (Google) (Arxiv) LLMs for User Interest Exploration in Large-scale Recommendation Systems](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2024%20%28Google%29%20%28Arxiv%29%20LLMs%20for%20User%20Interest%20Exploration%20in%20Large-scale%20Recommendation%20Systems.pdf) <br />
* [2024 (Google) (Arxiv) [CALRec] CALRec - Contrastive Alignment of Generative LLMs for Sequential Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2024%20%28Google%29%20%28Arxiv%29%20%5BCALRec%5D%20CALRec%20-%20Contrastive%20Alignment%20of%20Generative%20LLMs%20for%20Sequential%20Recommendation.pdf) <br />
* [2024 (Kuaishou) (Arxiv) [LEARN] LEARN - Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2024%20%28Kuaishou%29%20%28Arxiv%29%20%5BLEARN%5D%20LEARN%20-%20Knowledge%20Adaptation%20from%20Large%20Language%20Model%20to%20Recommendation%20for%20Practical%20Industrial%20Application.pdf) <br />
* [2024 (Kuaishou) (KDD) [NAR4Rec] Non-autoregressive Generative Models for Reranking Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2024%20%28Kuaishou%29%20%28KDD%29%20%5BNAR4Rec%5D%20Non-autoregressive%20Generative%20Models%20for%20Reranking%20Recommendation.pdf) <br />
* [2024 (Meta) (Arxiv) Unifying Generative and Dense Retrieval for Sequential Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2024%20%28Meta%29%20%28Arxiv%29%20Unifying%20Generative%20and%20Dense%20Retrieval%20for%20Sequential%20Recommendation.pdf) <br />
* [2024 (Meta) (Arxiv) [SUM] Scaling User Modeling - Large-scale Online User Representations for Ads Personalization in Meta](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2024%20%28Meta%29%20%28Arxiv%29%20%5BSUM%5D%20Scaling%20User%20Modeling%20-%20Large-scale%20Online%20User%20Representations%20for%20Ads%20Personalization%20in%20Meta.pdf) <br />
* [2025 (Alibaba) (Arxiv) Unlocking Scaling Law in Industrial Recommendation Systems with a Three-step Paradigm based Large User Model](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2025%20%28Alibaba%29%20%28Arxiv%29%20Unlocking%20Scaling%20Law%20in%20Industrial%20Recommendation%20Systems%20with%20a%20Three-step%20Paradigm%20based%20Large%20User%20Model.pdf) <br />
* [2025 (Alibaba) (Arxiv) [HeterRec] Hierarchical Causal Transformer with Heterogeneous Information for Expandable Sequential Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2025%20%28Alibaba%29%20%28Arxiv%29%20%5BHeterRec%5D%20Hierarchical%20Causal%20Transformer%20with%20Heterogeneous%20Information%20for%20Expandable%20Sequential%20Recommendation.pdf) <br />
* [2025 (Alibaba) (Arxiv) [URM] Large Language Models Are Universal Recommendation Learners](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2025%20%28Alibaba%29%20%28Arxiv%29%20%5BURM%5D%20Large%20Language%20Models%20Are%20Universal%20Recommendation%20Learners.pdf) <br />
* [2025 (Alibaba) (KDD) [GPSD] Scaling Transformers for Discriminative Recommendation via Generative Pretraining](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2025%20%28Alibaba%29%20%28KDD%29%20%5BGPSD%5D%20Scaling%20Transformers%20for%20Discriminative%20Recommendation%20via%20Generative%20Pretraining.pdf) <br />
* [2025 (Alibaba) (WWW) Explainable LLM-driven Multi-dimensional Distillation for E-Commerce Relevance Learning](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2025%20%28Alibaba%29%20%28WWW%29%20Explainable%20LLM-driven%20Multi-dimensional%20Distillation%20for%20E-Commerce%20Relevance%20Learning.pdf) <br />
* [2025 (Amazon) (Arxiv) SynerGen - Contextualized Generative Recommender for Unified Search and Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2025%20%28Amazon%29%20%28Arxiv%29%20SynerGen%20-%20Contextualized%20Generative%20Recommender%20for%20Unified%20Search%20and%20Recommendation.pdf) <br />
* [2025 (Arxiv) (Pinterest) [PinRec] PinRec - Outcome-Conditioned, Multi-Token Generative Retrieval for Industry-Scale Recommendation Systems](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2025%20%28Arxiv%29%20%28Pinterest%29%20%5BPinRec%5D%20PinRec%20-%20Outcome-Conditioned%2C%20Multi-Token%20Generative%20Retrieval%20for%20Industry-Scale%20Recommendation%20Systems.pdf) <br />
* [2025 (Arxiv) (Xiaohongshu) [GenRank] Towards Large-scale Generative Ranking](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2025%20%28Arxiv%29%20%28Xiaohongshu%29%20%5BGenRank%5D%20Towards%20Large-scale%20Generative%20Ranking.pdf) <br />
* [2025 (Baidu) (Arxiv) [COBRA] Sparse Meets Dense -Unified Generative Recommendations with Cascaded Sparse-Dense Representations](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2025%20%28Baidu%29%20%28Arxiv%29%20%5BCOBRA%5D%20Sparse%20Meets%20Dense%20-Unified%20Generative%20Recommendations%20with%20Cascaded%20Sparse-Dense%20Representations.pdf) <br />
* [2025 (Google) (Arxiv) User Feedback Alignment for LLM-powered Exploration in Large-scale Recommendation Systems](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2025%20%28Google%29%20%28Arxiv%29%20User%20Feedback%20Alignment%20for%20LLM-powered%20Exploration%20in%20Large-scale%20Recommendation%20Systems.pdf) <br />
* [2025 (Google) (Arxiv) [STAR] STAR - A Simple Training-free Approach for Recommendations using Large Language Models](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2025%20%28Google%29%20%28Arxiv%29%20%5BSTAR%5D%20STAR%20-%20A%20Simple%20Training-free%20Approach%20for%20Recommendations%20using%20Large%20Language%20Models.pdf) <br />
* [2025 (Kuaishou) (Arxiv) [GenSAR] Unified Generative Search and Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2025%20%28Kuaishou%29%20%28Arxiv%29%20%5BGenSAR%5D%20Unified%20Generative%20Search%20and%20Recommendation.pdf) <br />
* [2025 (Kuaishou) (Arxiv) [LARM] LLM-Alignment Live-Streaming Recommendationpdf](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2025%20%28Kuaishou%29%20%28Arxiv%29%20%5BLARM%5D%20LLM-Alignment%20Live-Streaming%20Recommendationpdf.pdf) <br />
* [2025 (Kuaishou) (Arxiv) [LEARN] LEARN - Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2025%20%28Kuaishou%29%20%28Arxiv%29%20%5BLEARN%5D%20LEARN%20-%20Knowledge%20Adaptation%20from%20Large%20Language%20Model%20to%20Recommendation%20for%20Practical%20Industrial%20Application.pdf) <br />
* [2025 (Kuaishou) (Arxiv) [OneRec-V2] OneRec Technical Report v2](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2025%20%28Kuaishou%29%20%28Arxiv%29%20%5BOneRec-V2%5D%20OneRec%20Technical%20Report%20v2.pdf) <br />
* [2025 (Kuaishou) (Arxiv) [OneRec] OneRec - Unifying Retrieve and Rank with Generative Recommender and Preference Alignment](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2025%20%28Kuaishou%29%20%28Arxiv%29%20%5BOneRec%5D%20OneRec%20-%20Unifying%20Retrieve%20and%20Rank%20with%20Generative%20Recommender%20and%20Preference%20Alignment.pdf) <br />
* [2025 (Kuaishou) (Arxiv) [OneRec] OneRec Technical Report](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2025%20%28Kuaishou%29%20%28Arxiv%29%20%5BOneRec%5D%20OneRec%20Technical%20Report.pdf) <br />
* [2025 (Meituan) (Arxiv) [DFGR] Action is All You Need - Dual-Flow Generative Ranking Network for Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2025%20%28Meituan%29%20%28Arxiv%29%20%5BDFGR%5D%20Action%20is%20All%20You%20Need%20-%20Dual-Flow%20Generative%20Ranking%20Network%20for%20Recommendation.pdf) <br />
* [2025 (Meituan) (Arxiv) [MTGR] MTGR - Industrial-Scale Generative Recommendation Framework in Meituan](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2025%20%28Meituan%29%20%28Arxiv%29%20%5BMTGR%5D%20MTGR%20-%20Industrial-Scale%20Generative%20Recommendation%20Framework%20in%20Meituan.pdf) <br />
* [2025 (Meituan) (Arxiv) [UniROM] One Model to Rank Them All - Unifying Online Advertising with End-to-End Learning](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2025%20%28Meituan%29%20%28Arxiv%29%20%5BUniROM%5D%20One%20Model%20to%20Rank%20Them%20All%20-%20Unifying%20Online%20Advertising%20with%20End-to-End%20Learning.pdf) <br />
* [2025 (Meta) (Arxiv) [HyperCast] Realizing Scaling Laws in Recommender Systems - A Foundation–Expert Paradigm for Hyperscale Model Deployment](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2025%20%28Meta%29%20%28Arxiv%29%20%5BHyperCast%5D%20Realizing%20Scaling%20Laws%20in%20Recommender%20Systems%20-%20A%20Foundation%E2%80%93Expert%20Paradigm%20for%20Hyperscale%20Model%20Deployment.pdf) <br />
* [2025 (Microsoft) (KDD)Towards Web-scale Recommendations with LLMs - From Quality-aware Ranking to Candidate Generation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2025%20%28Microsoft%29%20%28KDD%29Towards%20Web-scale%20Recommendations%20with%20LLMs%20-%20From%20Quality-aware%20Ranking%20to%20Candidate%20Generation.pdf) <br />
* [2025 (Pinterest) (Arxiv) [PinFM] PinFM - Foundation Model for User Activity Sequences at a Billion-scale Visual Discovery Platform](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2025%20%28Pinterest%29%20%28Arxiv%29%20%5BPinFM%5D%20PinFM%20-%20Foundation%20Model%20for%20User%20Activity%20Sequences%20at%20a%20Billion-scale%20Visual%20Discovery%20Platform.pdf) <br />
* [2025 (Tencent) (Arxiv) [GPR] GPR - Towards a Generative Pre-trained One-Model Paradigm for Large-Scale Advertising Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2025%20%28Tencent%29%20%28Arxiv%29%20%5BGPR%5D%20GPR%20-%20Towards%20a%20Generative%20Pre-trained%20One-Model%20Paradigm%20for%20Large-Scale%20Advertising%20Recommendation.pdf) <br />
* [2025 (eBay)  (Arxiv) LLMDistill4Ads - Using Cross-Encoders to Distill from LLM Signals for Advertiser Keyphrase Recommendations at eBay](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2025%20%28eBay%29%20%20%28Arxiv%29%20LLMDistill4Ads%20-%20Using%20Cross-Encoders%20to%20Distill%20from%20LLM%20Signals%20for%20Advertiser%20Keyphrase%20Recommendations%20at%20eBay.pdf) <br />
* [2025 （Bytedance) (Arxiv) [OneTrans] OneTrans - Unified Feature Interaction and Sequence Modeling with One Transformer in Industrial Recommender](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2025%20%EF%BC%88Bytedance%29%20%28Arxiv%29%20%5BOneTrans%5D%20OneTrans%20-%20Unified%20Feature%20Interaction%20and%20Sequence%20Modeling%20with%20One%20Transformer%20in%20Industrial%20Recommender.pdf) <br />
* [2025 （Meta) (Arxiv) [ExFM] External Large Foundation Model - How to Efficiently Serve Trillions of Parameters for Online Ads Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2025%20%EF%BC%88Meta%29%20%28Arxiv%29%20%5BExFM%5D%20External%20Large%20Foundation%20Model%20-%20How%20to%20Efficiently%20Serve%20Trillions%20of%20Parameters%20for%20Online%20Ads%20Recommendation.pdf) <br />
* [2026 (Alibaba) (Arxiv) [SORT] SORT - A Systematically Optimized Ranking Transformer for Industrial-scale Recommenders](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2026%20%28Alibaba%29%20%28Arxiv%29%20%5BSORT%5D%20SORT%20-%20A%20Systematically%20Optimized%20Ranking%20Transformer%20for%20Industrial-scale%20Recommenders.pdf) <br />
* [2026 (Bytedance) (Arxiv) [HyFormer] HyFormer - Revisiting the Roles of Sequence Modeling and Feature Interaction in CTR Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2026%20%28Bytedance%29%20%28Arxiv%29%20%5BHyFormer%5D%20HyFormer%20-%20Revisiting%20the%20Roles%20of%20Sequence%20Modeling%20and%20Feature%20Interaction%20in%20CTR%20Prediction.pdf) <br />
* [2026 (Bytedance) (Arxiv) [MixFormer] MixFormer - Co-Scaling Up Dense and Sequence in Industrial Recommenders](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2026%20%28Bytedance%29%20%28Arxiv%29%20%5BMixFormer%5D%20MixFormer%20-%20Co-Scaling%20Up%20Dense%20and%20Sequence%20in%20Industrial%20Recommenders.pdf) <br />
* [2026 (Kuaishou) (Arxiv) [GR4AD] Generative Recommendation for Large-Scale Advertising](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2026%20%28Kuaishou%29%20%28Arxiv%29%20%5BGR4AD%5D%20Generative%20Recommendation%20for%20Large-Scale%20Advertising.pdf) <br />
* [2026 (Meta) (Arxiv) [Kunlun] Kunlun - Establishing Scaling Laws for Massive-Scale Recommendation Systems through Unified Architecture Design](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2026%20%28Meta%29%20%28Arxiv%29%20%5BKunlun%5D%20Kunlun%20-%20Establishing%20Scaling%20Laws%20for%20Massive-Scale%20Recommendation%20Systems%20through%20Unified%20Architecture%20Design.pdf) <br />
* [2026 (Meta) (Arxiv) [WHALE] WHALE - A Scalable Unified Model for Recommendation with Wukong-HSTU Architecture](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2026%20%28Meta%29%20%28Arxiv%29%20%5BWHALE%5D%20WHALE%20-%20A%20Scalable%20Unified%20Model%20for%20Recommendation%20with%20Wukong-HSTU%20Architecture.pdf) <br />
* [2026 （Bytedance) (Arxiv) [MDL] MDL - A Unified Multi-Distribution Learner in Large-scale Industrial Recommendation through Tokenization](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/2026%20%EF%BC%88Bytedance%29%20%28Arxiv%29%20%5BMDL%5D%20MDL%20-%20A%20Unified%20Multi-Distribution%20Learner%20in%20Large-scale%20Industrial%20Recommendation%20through%20Tokenization.pdf) <br />
* [](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/LLM_Ranking/LLM_MultiModal_Ranking) <br />

#### Loss
* [2015 (Twitter) (KDD) Click-through Prediction for Advertising in Twitter Timeline](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Loss/2015%20%28Twitter%29%20%28KDD%29%20Click-through%20Prediction%20for%20Advertising%20in%20Twitter%20Timeline.pdf) <br />
* [2022 (Google) (KDD) Scale Calibration of Deep Ranking Models](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Loss/2022%20%28Google%29%20%28KDD%29%20Scale%20Calibration%20of%20Deep%20Ranking%20Models.pdf) <br />
* [2023 (Alibaba) (KDD) Joint Optimization of Ranking and Calibration with Contextualized Hybrid Model](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Loss/2023%20%28Alibaba%29%20%28KDD%29%20Joint%20Optimization%20of%20Ranking%20and%20Calibration%20with%20Contextualized%20Hybrid%20Model.pdf) <br />
* [2023 (Google) (CIKM) Regression Compatible Listwise Objectives for Calibrated Ranking with Binary Relevance](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Loss/2023%20%28Google%29%20%28CIKM%29%20Regression%20Compatible%20Listwise%20Objectives%20for%20Calibrated%20Ranking%20with%20Binary%20Relevance.pdf) <br />
* [2024 (Tencent) (KDD) Understanding the Ranking Loss for Recommendation with Sparse User Feedback](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Loss/2024%20%28Tencent%29%20%28KDD%29%20Understanding%20the%20Ranking%20Loss%20for%20Recommendation%20with%20Sparse%20User%20Feedback.pdf) <br />

#### Multi-Modal
* [2018 (Alibaba) (CIKM) [Image CTR] Image Matters - Visually Modeling User Behaviors Using Advanced Model Server](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-Modal/2018%20%28Alibaba%29%20%28CIKM%29%20%5BImage%20CTR%5D%20Image%20Matters%20-%20Visually%20Modeling%20User%20Behaviors%20Using%20Advanced%20Model%20Server.pdf) <br />
* [2024 (Alibaba) (CIKM) Enhancing Taobao Display Advertising with Multimodal Representations - Challenges, Approaches and Insights](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-Modal/2024%20%28Alibaba%29%20%28CIKM%29%20Enhancing%20Taobao%20Display%20Advertising%20with%20Multimodal%20Representations%20-%20Challenges%2C%20Approaches%20and%20Insights.pdf) <br />
* [2026 (Alibaba) (WSDM) [MOON] MOON - Generative MLLM-based Multimodal Representation Learning for E-commerce Product Understanding](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-Modal/2026%20%28Alibaba%29%20%28WSDM%29%20%5BMOON%5D%20MOON%20-%20Generative%20MLLM-based%20Multimodal%20Representation%20Learning%20for%20E-commerce%20Product%20Understanding.pdf) <br />

#### Multi-domain-Multi-Scenario
* [2014 (TASLP) * [LHUC] Learning Hidden Unit Contributions for Unsupervised Acoustic Model Adaptation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-domain-Multi-Scenario/2014%20%28TASLP%29%20%2A%20%5BLHUC%5D%20Learning%20Hidden%20Unit%20Contributions%20for%20Unsupervised%20Acoustic%20Model%20Adaptation.pdf) <br />
* [2015 (Microsoft) (WWW) A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-domain-Multi-Scenario/2015%20%28Microsoft%29%20%28WWW%29%20A%20Multi-View%20Deep%20Learning%20Approach%20for%20Cross%20Domain%20User%20Modeling%20in%20Recommendation%20Systems.pdf) <br />
* [2018 (Google) (KDD) ** [MMoE] Modeling task relationships in multi-task learning with multi-gate mixture-of-experts](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-domain-Multi-Scenario/2018%20%28Google%29%20%28KDD%29%20%2A%2A%20%5BMMoE%5D%20Modeling%20task%20relationships%20in%20multi-task%20learning%20with%20multi-gate%20mixture-of-experts.pdf) <br />
* [2019 (Alibaba) (CIKM) [WE-CAN] Cross-domain Attention Network with Wasserstein Regularizers for E-commerce Search](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-domain-Multi-Scenario/2019%20%28Alibaba%29%20%28CIKM%29%20%5BWE-CAN%5D%20Cross-domain%20Attention%20Network%20with%20Wasserstein%20Regularizers%20for%20E-commerce%20Search.pdf) <br />
* [2020 (Alibaba) (Arxiv) [SAML] Scenario-aware and Mutual-based approach for Multi-scenario Recommendation in E-Commerce](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-domain-Multi-Scenario/2020%20%28Alibaba%29%20%28Arxiv%29%20%5BSAML%5D%20Scenario-aware%20and%20Mutual-based%20approach%20for%20Multi-scenario%20Recommendation%20in%20E-Commerce.pdf) <br />
* [2020 (Alibaba) (CIKM) [HMoE] Improving Multi-Scenario Learning to Rank in E-commerce by Exploiting Task Relationships in the Label Space](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-domain-Multi-Scenario/2020%20%28Alibaba%29%20%28CIKM%29%20%5BHMoE%5D%20Improving%20Multi-Scenario%20Learning%20to%20Rank%20in%20E-commerce%20by%20Exploiting%20Task%20Relationships%20in%20the%20Label%20Space.pdf) <br />
* [2020 (Alibaba)(CIKM) [MiNet] MiNet - Mixed Interest Network for Cross-Domain Click-Through Rate Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-domain-Multi-Scenario/2020%20%28Alibaba%29%28CIKM%29%20%5BMiNet%5D%20MiNet%20-%20Mixed%20Interest%20Network%20for%20Cross-Domain%20Click-Through%20Rate%20Prediction.pdf) <br />
* [2020 (Tencent) (Recsys) **  [PLE] Progressive Layered Extraction (PLE) - A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-domain-Multi-Scenario/2020%20%28Tencent%29%20%28Recsys%29%20%2A%2A%20%20%5BPLE%5D%20Progressive%20Layered%20Extraction%20%28PLE%29%20-%20A%20Novel%20Multi-Task%20Learning%20%28MTL%29%20Model%20for%20Personalized%20Recommendations.pdf) <br />
* [2021 (Alibaba) (CIKM) * [ZEUS] Self-Supervised Learning on Users’ Spontaneous Behaviors for Multi-Scenario Ranking in E-commerce](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-domain-Multi-Scenario/2021%20%28Alibaba%29%20%28CIKM%29%20%2A%20%5BZEUS%5D%20Self-Supervised%20Learning%20on%20Users%E2%80%99%20Spontaneous%20Behaviors%20for%20Multi-Scenario%20Ranking%20in%20E-commerce.pdf) <br />
* [2021 (Alibaba) (CIKM) ** [STAR] One Model to Serve All - Star Topology Adaptive Recommender for Multi-Domain CTR Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-domain-Multi-Scenario/2021%20%28Alibaba%29%20%28CIKM%29%20%2A%2A%20%5BSTAR%5D%20One%20Model%20to%20Serve%20All%20-%20Star%20Topology%20Adaptive%20Recommender%20for%20Multi-Domain%20CTR%20Prediction.pdf) <br />
* [2021 (Google) (ICLR) HyperGrid Transformers - Towards A Single Model for Multiple Tasks](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-domain-Multi-Scenario/2021%20%28Google%29%20%28ICLR%29%20HyperGrid%20Transformers%20-%20Towards%20A%20Single%20Model%20for%20Multiple%20Tasks.pdf) <br />
* [2021 (Kwai) (Arxiv) [POSO] POSO - Personalized Cold Start Modules for Large-scale Recommender Systems](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-domain-Multi-Scenario/2021%20%28Kwai%29%20%28Arxiv%29%20%5BPOSO%5D%20POSO%20-%20Personalized%20Cold%20Start%20Modules%20for%20Large-scale%20Recommender%20Systems.pdf) <br />
* [2022 (Alibaba) (CIKM) AdaSparse - Learning Adaptively Sparse Structures for Multi-Domain Click-Through Rate Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-domain-Multi-Scenario/2022%20%28Alibaba%29%20%28CIKM%29%20AdaSparse%20-%20Learning%20Adaptively%20Sparse%20Structures%20for%20Multi-Domain%20Click-Through%20Rate%20Prediction.pdf) <br />
* [2022 (Alibaba) (NIPS) ** [APG] APG - Adaptive Parameter Generation Network for Click-Through Rate Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-domain-Multi-Scenario/2022%20%28Alibaba%29%20%28NIPS%29%20%2A%2A%20%5BAPG%5D%20APG%20-%20Adaptive%20Parameter%20Generation%20Network%20for%20Click-Through%20Rate%20Prediction.pdf) <br />
* [2023 (Alibaba) (CIKM) [HC2] Hybrid Contrastive Constraints for Multi-Scenario Ad Ranking](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-domain-Multi-Scenario/2023%20%28Alibaba%29%20%28CIKM%29%20%5BHC2%5D%20Hybrid%20Contrastive%20Constraints%20for%20Multi-Scenario%20Ad%20Ranking.pdf) <br />
* [2023 (Alibaba) (CIKM) [MMN] Masked Multi-Domain Network - Multi-Type and Multi-Scenario Conversion Rate Prediction with a Single Model](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-domain-Multi-Scenario/2023%20%28Alibaba%29%20%28CIKM%29%20%5BMMN%5D%20Masked%20Multi-Domain%20Network%20-%20Multi-Type%20and%20Multi-Scenario%20Conversion%20Rate%20Prediction%20with%20a%20Single%20Model.pdf) <br />
* [2023 (Alibaba) (CIKM) [Rec4Ad] Rec4Ad - A Free Lunch to Mitigate Sample Selection Bias for Ads CTR Prediction in Taobao](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-domain-Multi-Scenario/2023%20%28Alibaba%29%20%28CIKM%29%20%5BRec4Ad%5D%20Rec4Ad%20-%20A%20Free%20Lunch%20to%20Mitigate%20Sample%20Selection%20Bias%20for%20Ads%20CTR%20Prediction%20in%20Taobao.pdf) <br />
* [2023 (Alibaba) (SIGIR) [MARIA] Multi-Scenario Ranking with Adaptive Feature Learning](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-domain-Multi-Scenario/2023%20%28Alibaba%29%20%28SIGIR%29%20%5BMARIA%5D%20Multi-Scenario%20Ranking%20with%20Adaptive%20Feature%20Learning.pdf) <br />
* [2023 (CIKM) [HAMUR] HAMUR - Hyper Adapter for Multi-Domain Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-domain-Multi-Scenario/2023%20%28CIKM%29%20%5BHAMUR%5D%20HAMUR%20-%20Hyper%20Adapter%20for%20Multi-Domain%20Recommendation.pdf) <br />
* [2023 (Huawei) (CIKM) [DFFM] DFFM - Domain Facilitated Feature Modeling for CTR Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-domain-Multi-Scenario/2023%20%28Huawei%29%20%28CIKM%29%20%5BDFFM%5D%20DFFM%20-%20Domain%20Facilitated%20Feature%20Modeling%20for%20CTR%20Prediction.pdf) <br />
* [2023 (Kuaishou) (KDD) *  [PEPNet] PEPNet - Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-domain-Multi-Scenario/2023%20%28Kuaishou%29%20%28KDD%29%20%2A%20%20%5BPEPNet%5D%20PEPNet%20-%20Parameter%20and%20Embedding%20Personalized%20Network%20for%20Infusing%20with%20Personalized%20Prior%20Information.pdf) <br />
* [2023 (Tencent) (KDD) Scenario-Adaptive Feature Interaction for Click-Through Rate Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-domain-Multi-Scenario/2023%20%28Tencent%29%20%28KDD%29%20Scenario-Adaptive%20Feature%20Interaction%20for%20Click-Through%20Rate%20Prediction.pdf) <br />
* [2024 (Alibaba) (CIKM) * [MultiLoRA] MultiLoRA - Multi-Directional Low-Rank Adaptation for Multi-Domain Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-domain-Multi-Scenario/2024%20%28Alibaba%29%20%28CIKM%29%20%2A%20%5BMultiLoRA%5D%20MultiLoRA%20-%20Multi-Directional%20Low-Rank%20Adaptation%20for%20Multi-Domain%20Recommendation.pdf) <br />
* [2024 (Alibaba) (RecSys) * [MLoRA] MLoRA - Multi-Domain Low-Rank Adaptive Network for Click-Through Rate Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-domain-Multi-Scenario/2024%20%28Alibaba%29%20%28RecSys%29%20%2A%20%5BMLoRA%5D%20MLoRA%20-%20Multi-Domain%20Low-Rank%20Adaptive%20Network%20for%20Click-Through%20Rate%20Prediction.pdf) <br />
* [2024 (Kuaishou) (SIGIR) [M3oE] M3oE - Multi-Domain Multi-Task Mixture-of-Experts Recommendation Framework](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-domain-Multi-Scenario/2024%20%28Kuaishou%29%20%28SIGIR%29%20%5BM3oE%5D%20M3oE%20-%20Multi-Domain%20Multi-Task%20Mixture-of-Experts%20Recommendation%20Framework.pdf) <br />
* [2024 (Tencent) (KDD) [LCN] Cross-Domain LifeLong Sequential Modeling for Online Click-Through Rate Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-domain-Multi-Scenario/2024%20%28Tencent%29%20%28KDD%29%20%5BLCN%5D%20Cross-Domain%20LifeLong%20Sequential%20Modeling%20for%20Online%20Click-Through%20Rate%20Prediction.pdf) <br />
* [2024 (WSDM) Exploring Adapter-based Transfer Learning for Recommender Systems - Empirical Studies and Practical Insights](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-domain-Multi-Scenario/2024%20%28WSDM%29%20Exploring%20Adapter-based%20Transfer%20Learning%20for%20Recommender%20Systems%20-%20Empirical%20Studies%20and%20Practical%20Insights.pdf) <br />
* [2025 (Kuaishou) (KDD) [HoME] HoME - Hierarchy of Multi-Gate Experts for Multi-Task Learning at Kuaishou](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-domain-Multi-Scenario/2025%20%28Kuaishou%29%20%28KDD%29%20%5BHoME%5D%20HoME%20-%20Hierarchy%20of%20Multi-Gate%20Experts%20for%20Multi-Task%20Learning%20at%20Kuaishou.pdf) <br />

#### Multi-task
* [(2018) (ICML) GradNorm - Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/%282018%29%20%28ICML%29%20GradNorm%20-%20Gradient%20Normalization%20for%20Adaptive%20Loss%20Balancing%20in%20Deep%20Multitask%20Networks.pdf) <br />
* [2014 (TASLP) [LHUC] Learning Hidden Unit Contributions for Unsupervised Acoustic Model Adaptation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2014%20%28TASLP%29%20%5BLHUC%5D%20Learning%20Hidden%20Unit%20Contributions%20for%20Unsupervised%20Acoustic%20Model%20Adaptation.pdf) <br />
* [2017 (Google) (ICLR) [Sparsely-Gated MOE] Outrageously large neural networks - The sparsely-gated mixture-of-experts layer](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2017%20%28Google%29%20%28ICLR%29%20%5BSparsely-Gated%20MOE%5D%20Outrageously%20large%20neural%20networks%20-%20The%20sparsely-gated%20mixture-of-experts%20layer.pdf) <br />
* [2018 (Alibaba) (KDD) [DUPN] Perceive Your Users in Depth - Learning Universal User Representations from Multiple E-commerce Tasks](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2018%20%28Alibaba%29%20%28KDD%29%20%5BDUPN%5D%20Perceive%20Your%20Users%20in%20Depth%20-%20Learning%20Universal%20User%20Representations%20from%20Multiple%20E-commerce%20Tasks.pdf) <br />
* [2018 (Alibaba) (SIGIR) [ESMM] Entire Space Multi-Task Model - An Effective Approach for Estimating Post-Click Conversion Rate](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2018%20%28Alibaba%29%20%28SIGIR%29%20%5BESMM%5D%20Entire%20Space%20Multi-Task%20Model%20-%20An%20Effective%20Approach%20for%20Estimating%20Post-Click%20Conversion%20Rate.pdf) <br />
* [2018 (CVPR) Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2018%20%28CVPR%29%20Multi-Task%20Learning%20Using%20Uncertainty%20to%20Weigh%20Losses%20for%20Scene%20Geometry%20and%20Semantics.pdf) <br />
* [2018 (Google) (KDD) ** [MMoE] Modeling task relationships in multi-task learning with multi-gate mixture-of-experts](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2018%20%28Google%29%20%28KDD%29%20%2A%2A%20%5BMMoE%5D%20Modeling%20task%20relationships%20in%20multi-task%20learning%20with%20multi-gate%20mixture-of-experts.pdf) <br />
* [2019 (Alibaba) (CIKM) Multi-task based Sales Predictions for Online Promotions](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2019%20%28Alibaba%29%20%28CIKM%29%20Multi-task%20based%20Sales%20Predictions%20for%20Online%20Promotions.pdf) <br />
* [2019 (Alibaba) (Recys) A Pareto-Eficient Algorithm for Multiple Objective Optimization in E-Commerce Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2019%20%28Alibaba%29%20%28Recys%29%20A%20Pareto-Eficient%20Algorithm%20for%20Multiple%20Objective%20Optimization%20in%20E-Commerce%20Recommendation.pdf) <br />
* [2019 (Google) (AAAI) SNR Sub-Network Routing for Flexible Parameter Sharing in Multi-Task Learning](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2019%20%28Google%29%20%28AAAI%29%20SNR%20Sub-Network%20Routing%20for%20Flexible%20Parameter%20Sharing%20in%20Multi-Task%20Learning.pdf) <br />
* [2019 (Google) (Recsys) ** [Youtube Multi-task] Recommending what video to watch next - a multitask ranking system](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2019%20%28Google%29%20%28Recsys%29%20%2A%2A%20%5BYoutube%20Multi-task%5D%20Recommending%20what%20video%20to%20watch%20next%20-%20a%20multitask%20ranking%20system.pdf) <br />
* [2019 (NIPS) Pareto Multi-Task Learning](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2019%20%28NIPS%29%20Pareto%20Multi-Task%20Learning.pdf) <br />
* [2020 (Alibaba) (SIGIR) [ESM2] Entire Space Multi-Task Modeling via Post-Click Behavior Decomposition for Conversion Rate Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2020%20%28Alibaba%29%20%28SIGIR%29%20%5BESM2%5D%20Entire%20Space%20Multi-Task%20Modeling%20via%20Post-Click%20Behavior%20Decomposition%20for%20Conversion%20Rate%20Prediction.pdf) <br />
* [2020 (Alibaba) (WWW) Large-scale Causal Approaches to Debiasing Post-click Conversion Rate Estimation with Multi-task Learning](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2020%20%28Alibaba%29%20%28WWW%29%20Large-scale%20Causal%20Approaches%20to%20Debiasing%20Post-click%20Conversion%20Rate%20Estimation%20with%20Multi-task%20Learning.pdf) <br />
* [2020 (Amazon) (WWW) Multi-Objective Ranking Optimization for Product Search Using Stochastic Label Aggregation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2020%20%28Amazon%29%20%28WWW%29%20Multi-Objective%20Ranking%20Optimization%20for%20Product%20Search%20Using%20Stochastic%20Label%20Aggregation.pdf) <br />
* [2020 (Google) (KDD) [MoSE] Multitask Mixture of Sequential Experts for User Activity Streams](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2020%20%28Google%29%20%28KDD%29%20%5BMoSE%5D%20Multitask%20Mixture%20of%20Sequential%20Experts%20for%20User%20Activity%20Streams.pdf) <br />
* [2020 (JD) (CIKM) *[DMT] Deep Multifaceted Transformers for Multi-objective Ranking in Large-Scale E-commerce Recommender Systems](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2020%20%28JD%29%20%28CIKM%29%20%2A%5BDMT%5D%20Deep%20Multifaceted%20Transformers%20for%20Multi-objective%20Ranking%20in%20Large-Scale%20E-commerce%20Recommender%20Systems.pdf) <br />
* [2020 (Tencent) (Recsys) **  [PLE] Progressive Layered Extraction (PLE) - A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2020%20%28Tencent%29%20%28Recsys%29%20%2A%2A%20%20%5BPLE%5D%20Progressive%20Layered%20Extraction%20%28PLE%29%20-%20A%20Novel%20Multi-Task%20Learning%20%28MTL%29%20Model%20for%20Personalized%20Recommendations.pdf) <br />
* [2021 (Alibaba) (SIGIR) [HM3] Hierarchically Modeling Micro and Macro Behaviors via Multi-Task Learning for Conversion Rate Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2021%20%28Alibaba%29%20%28SIGIR%29%20%5BHM3%5D%20Hierarchically%20Modeling%20Micro%20and%20Macro%20Behaviors%20via%20Multi-Task%20Learning%20for%20Conversion%20Rate%20Prediction.pdf) <br />
* [2021 (Alibaba) (SIGIR) [MSSM] MSSM - A Multiple-level Sparse Sharing Model for Efficient Multi-Task Learning](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2021%20%28Alibaba%29%20%28SIGIR%29%20%5BMSSM%5D%20MSSM%20-%20A%20Multiple-level%20Sparse%20Sharing%20Model%20for%20Efficient%20Multi-Task%20Learning.pdf) <br />
* [2021 (Baidu) (SIGIR) [GemNN] GemNN - Gating-Enhanced Multi-Task Neural Networks with Feature Interaction Learning for CTR Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2021%20%28Baidu%29%20%28SIGIR%29%20%5BGemNN%5D%20GemNN%20-%20Gating-Enhanced%20Multi-Task%20Neural%20Networks%20with%20Feature%20Interaction%20Learning%20for%20CTR%20Prediction.pdf) <br />
* [2021 (Google) (Arxiv) [DSelect-k] DSelect-k Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2021%20%28Google%29%20%28Arxiv%29%20%5BDSelect-k%5D%20DSelect-k%20Differentiable%20Selection%20in%20the%20Mixture%20of%20Experts%20with%20Applications%20to%20Multi-Task%20Learning.pdf) <br />
* [2021 (Google) (KDD) Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2021%20%28Google%29%20%28KDD%29%20Understanding%20and%20Improving%20Fairness-Accuracy%20Trade-offs%20in%20Multi-Task%20Learning.pdf) <br />
* [2021 (JD) (ICDE) Adversarial Mixture Of Experts with Category Hierarchy Soft Constraint](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2021%20%28JD%29%20%28ICDE%29%20Adversarial%20Mixture%20Of%20Experts%20with%20Category%20Hierarchy%20Soft%20Constraint.pdf) <br />
* [2021 (Meituan) (KDD) Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2021%20%28Meituan%29%20%28KDD%29%20Modeling%20the%20Sequential%20Dependence%20among%20Audience%20Multi-step%20Conversions%20with%20Multi-task%20Learning%20in%20Targeted%20Display%20Advertising.pdf) <br />
* [2021 (Tencent) (Arxiv) Mixture of Virtual-Kernel Experts for Multi-Objective User Profile Modeling](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2021%20%28Tencent%29%20%28Arxiv%29%20Mixture%20of%20Virtual-Kernel%20Experts%20for%20Multi-Objective%20User%20Profile%20Modeling.pdf) <br />
* [2021 (Tencent) (WWW) Personalized Approximate Pareto-Efficient Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2021%20%28Tencent%29%20%28WWW%29%20Personalized%20Approximate%20Pareto-Efficient%20Recommendation.pdf) <br />
* [2022 (Google) (WWW) Can Small Heads Help? Understanding and Improving Multi-Task Generalization](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2022%20%28Google%29%20%28WWW%29%20Can%20Small%20Heads%20Help%3F%20Understanding%20and%20Improving%20Multi-Task%20Generalization.pdf) <br />
* [2023 (Airbnb) (KDD) Optimizing Airbnb Search Journey with Multi-task Learning](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2023%20%28Airbnb%29%20%28KDD%29%20Optimizing%20Airbnb%20Search%20Journey%20with%20Multi-task%20Learning.pdf) <br />
* [2023 (Alibaba) (CIKM) [DTRN] Deep Task-specific Bottom Representation Network for Multi-Task Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2023%20%28Alibaba%29%20%28CIKM%29%20%5BDTRN%5D%20Deep%20Task-specific%20Bottom%20Representation%20Network%20for%20Multi-Task%20Recommendation.pdf) <br />
* [2023 (Google) (CIKM) Multitask Ranking System for Immersive Feed and No More Clicks - A Case Study of Short-Form Video Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2023%20%28Google%29%20%28CIKM%29%20Multitask%20Ranking%20System%20for%20Immersive%20Feed%20and%20No%20More%20Clicks%20-%20A%20Case%20Study%20of%20Short-Form%20Video%20Recommendation.pdf) <br />
* [2023 (Google) (KDD) Improving Training Stability for Multitask Ranking Models in Recommender Systems](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2023%20%28Google%29%20%28KDD%29%20Improving%20Training%20Stability%20for%20Multitask%20Ranking%20Models%20in%20Recommender%20Systems.pdf) <br />
* [2023 (Meta) (KDD) AdaTT - Adaptive Task-to-Task Fusion Network for Multitask Learning in Recommendations](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2023%20%28Meta%29%20%28KDD%29%20AdaTT%20-%20Adaptive%20Task-to-Task%20Fusion%20Network%20for%20Multitask%20Learning%20in%20Recommendations.pdf) <br />
* [2024 (Airbnb) (KDD) Multi-objective Learning to Rank by Model Distillation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2024%20%28Airbnb%29%20%28KDD%29%20Multi-objective%20Learning%20to%20Rank%20by%20Model%20Distillation.pdf) <br />
* [2024 (Kuaishou) (KDD) [GradCraft] GradCraft - Elevating Multi-task Recommendations through Holistic Gradient Crafting](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2024%20%28Kuaishou%29%20%28KDD%29%20%5BGradCraft%5D%20GradCraft%20-%20Elevating%20Multi-task%20Recommendations%20through%20Holistic%20Gradient%20Crafting.pdf) <br />
* [2024 (Kuaishou) [HoME] HoME - Hierarchy of Multi-Gate Experts for Multi-Task Learning at Kuaishou](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2024%20%28Kuaishou%29%20%5BHoME%5D%20HoME%20-%20Hierarchy%20of%20Multi-Gate%20Experts%20for%20Multi-Task%20Learning%20at%20Kuaishou.pdf) <br />
* [2024 (Shopee) (KDD) [ResFlow] Residual Multi-Task Learner for Applied Ranking](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2024%20%28Shopee%29%20%28KDD%29%20%5BResFlow%5D%20Residual%20Multi-Task%20Learner%20for%20Applied%20Ranking.pdf) <br />
* [2024 (Tencent) (KDD) [STEM] Ads Recommendation in a Collapsed and Entangled World](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2024%20%28Tencent%29%20%28KDD%29%20%5BSTEM%5D%20Ads%20Recommendation%20in%20a%20Collapsed%20and%20Entangled%20World.pdf) <br />
* [2025 (Baidu) (KDD) [RankExpert] RankExpert - A Mixture of Textual-and-Behavioral Experts for Multi-Objective Learning-to-Rank in Web Search](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2025%20%28Baidu%29%20%28KDD%29%20%5BRankExpert%5D%20RankExpert%20-%20A%20Mixture%20of%20Textual-and-Behavioral%20Experts%20for%20Multi-Objective%20Learning-to-Rank%20in%20Web%20Search.pdf) <br />
* [2025 (Byetedance) (CIKM) [PMTA] PMTA - Perception-Aware Multi-Task Transformer Network for Personalized Multi-Domain Adaptation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2025%20%28Byetedance%29%20%28CIKM%29%20%5BPMTA%5D%20PMTA%20-%20Perception-Aware%20Multi-Task%20Transformer%20Network%20for%20Personalized%20Multi-Domain%20Adaptation.pdf) <br />
* [2025 （Alibaba) (CIKM) [MAL] See Beyond a Single View - Multi-Attribution Learning Leads to Better Conversion Rate Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2025%20%EF%BC%88Alibaba%29%20%28CIKM%29%20%5BMAL%5D%20See%20Beyond%20a%20Single%20View%20-%20Multi-Attribution%20Learning%20Leads%20to%20Better%20Conversion%20Rate%20Prediction.pdf) <br />
* [2026 (Bytedance) (Arxiv) [MDL] MDL - A Unified Multi-Distribution Learner in Large-scale Industrial Recommendation through Tokenization](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Multi-task/2026%20%28Bytedance%29%20%28Arxiv%29%20%5BMDL%5D%20MDL%20-%20A%20Unified%20Multi-Distribution%20Learner%20in%20Large-scale%20Industrial%20Recommendation%20through%20Tokenization.pdf) <br />

#### ParameterServer
* [2014 (Baidu) (OSDI) Scaling Distributed Machine Learning with the Parameter Server](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/ParameterServer/2014%20%28Baidu%29%20%28OSDI%29%20Scaling%20Distributed%20Machine%20Learning%20with%20the%20Parameter%20Server.pdf) <br />
* [2019 (Alibaba) (DLP-KDD) [XDL] XDL - An Industrial Deep Learning Framework for High-dimensional Sparse Data](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/ParameterServer/2019%20%28Alibaba%29%20%28DLP-KDD%29%20%5BXDL%5D%20XDL%20-%20An%20Industrial%20Deep%20Learning%20Framework%20for%20High-dimensional%20Sparse%20Data.pdf) <br />
* [2020 (Bytedance) (OSDI) [BytePS] A Unified Architecture for Accelerating Distributed DNN Training in Heterogeneous GPU:CPU Clusters](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/ParameterServer/2020%20%28Bytedance%29%20%28OSDI%29%20%5BBytePS%5D%20A%20Unified%20Architecture%20for%20Accelerating%20Distributed%20DNN%20Training%20in%20Heterogeneous%20GPU%3ACPU%20Clusters.pdf) <br />
* [2022 (Kuaishou) (KDD) [Persia] Persia - An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameters](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/ParameterServer/2022%20%28Kuaishou%29%20%28KDD%29%20%5BPersia%5D%20Persia%20-%20An%20Open%2C%20Hybrid%20System%20Scaling%20Deep%20Learning-based%20Recommenders%20up%20to%20100%20Trillion%20Parameters.pdf) <br />

#### Pre-training
* [2019 (Alibaba) (IJCAI) [DeepMCP] Representation Learning-Assisted Click-Through Rate Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Pre-training/2019%20%28Alibaba%29%20%28IJCAI%29%20%5BDeepMCP%5D%20Representation%20Learning-Assisted%20Click-Through%20Rate%20Prediction.pdf) <br />
* [2019 (SIGIR) [BERT4Rec] (Alibaba) (SIGIR2019) BERT4Rec - Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Pre-training/2019%20%28SIGIR%29%20%5BBERT4Rec%5D%20%28Alibaba%29%20%28SIGIR2019%29%20BERT4Rec%20-%20Sequential%20Recommendation%20with%20Bidirectional%20Encoder%20Representations%20from%20Transformer.pdf) <br />

#### Sequence-Modeling
* [2016 (Google) (RecSys) **[Youtube DNN] Deep Neural Networks for YouTube Recommendations](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Sequence-Modeling/2016%20%28Google%29%20%28RecSys%29%20%2A%2A%5BYoutube%20DNN%5D%20Deep%20Neural%20Networks%20for%20YouTube%20Recommendations.pdf) <br />
* [2017 (Google) (NIPS) ** Attention Is All You Need](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Sequence-Modeling/2017%20%28Google%29%20%28NIPS%29%20%2A%2A%20Attention%20Is%20All%20You%20Need.pdf) <br />
* [2018 (Alibaba) (KDD) **[DIN] Deep Interest Network for Click-Through Rate Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Sequence-Modeling/2018%20%28Alibaba%29%20%28KDD%29%20%2A%2A%5BDIN%5D%20Deep%20Interest%20Network%20for%20Click-Through%20Rate%20Prediction.pdf) <br />
* [2018 (Alibaba) (KDD) [DUPN] Perceive Your Users in Depth - Learning Universal User Representations from Multiple E-commerce Tasks](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Sequence-Modeling/2018%20%28Alibaba%29%20%28KDD%29%20%5BDUPN%5D%20Perceive%20Your%20Users%20in%20Depth%20-%20Learning%20Universal%20User%20Representations%20from%20Multiple%20E-commerce%20Tasks.pdf) <br />
* [2019 (Alibaba) (AAAI) **[DIEN] Deep Interest Evolution Network for Click-Through Rate Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Sequence-Modeling/2019%20%28Alibaba%29%20%28AAAI%29%20%2A%2A%5BDIEN%5D%20Deep%20Interest%20Evolution%20Network%20for%20Click-Through%20Rate%20Prediction.pdf) <br />
* [2019 (Alibaba) (IJCAI) [DSIN] Deep Session Interest Network for Click-Through Rate Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Sequence-Modeling/2019%20%28Alibaba%29%20%28IJCAI%29%20%5BDSIN%5D%20Deep%20Session%20Interest%20Network%20for%20Click-Through%20Rate%20Prediction.pdf) <br />
* [2019 (Alibaba) (KDD) [BST] Behavior Sequence Transformer for E-commerce Recommendation in Alibaba](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Sequence-Modeling/2019%20%28Alibaba%29%20%28KDD%29%20%5BBST%5D%20Behavior%20Sequence%20Transformer%20for%20E-commerce%20Recommendation%20in%20Alibaba.pdf) <br />
* [2019 (Tencent) (KDD) [RALM] TReal-time Attention Based Look-alike Model for Recommender System](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Sequence-Modeling/2019%20%28Tencent%29%20%28KDD%29%20%5BRALM%5D%20TReal-time%20Attention%20Based%20Look-alike%20Model%20for%20Recommender%20System.pdf) <br />
* [2020 (JD) (CIKM) **[DMT] Deep Multifaceted Transformers for Multi-objective Ranking in Large-Scale E-commerce Recommender Systems](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Sequence-Modeling/2020%20%28JD%29%20%28CIKM%29%20%2A%2A%5BDMT%5D%20Deep%20Multifaceted%20Transformers%20for%20Multi-objective%20Ranking%20in%20Large-Scale%20E-commerce%20Recommender%20Systems.pdf) <br />
* [2020 (JD) (NIPS) [KFAtt] Kalman Filtering Attention for User Behavior Modeling in CTR Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Sequence-Modeling/2020%20%28JD%29%20%28NIPS%29%20%5BKFAtt%5D%20Kalman%20Filtering%20Attention%20for%20User%20Behavior%20Modeling%20in%20CTR%20Prediction.pdf) <br />
* [2020 (JD) (WSDM) [HUP] Hierarchical User Profiling for E-commerce Recommender Systems](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Sequence-Modeling/2020%20%28JD%29%20%28WSDM%29%20%5BHUP%5D%20Hierarchical%20User%20Profiling%20for%20E-commerce%20Recommender%20Systems.pdf) <br />
* [2022 (Alibaba) (WSDM) [RACP] Modeling Users’ Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Sequence-Modeling/2022%20%28Alibaba%29%20%28WSDM%29%20%5BRACP%5D%20Modeling%20Users%E2%80%99%20Contextualized%20Page-wise%20Feedback%20for%20Click-Through%20Rate%20Prediction%20in%20E-commerce%20Search.pdf) <br />
* [2022 (JD) (WWW) Implicit User Awareness Modeling via Candidate Items for CTR Prediction in Search Ads](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Sequence-Modeling/2022%20%28JD%29%20%28WWW%29%20Implicit%20User%20Awareness%20Modeling%20via%20Candidate%20Items%20for%20CTR%20Prediction%20in%20Search%20Ads.pdf) <br />
* [2022 (WWW) [FMLP] Filter-enhanced MLP is All You Need for Sequential Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Sequence-Modeling/2022%20%28WWW%29%20%5BFMLP%5D%20Filter-enhanced%20MLP%20is%20All%20You%20Need%20for%20Sequential%20Recommendation.pdf) <br />
* [2023 (Meituan) (CIKM) [DCIN] Deep Context Interest Network for Click-Through Rate Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Sequence-Modeling/2023%20%28Meituan%29%20%28CIKM%29%20%5BDCIN%5D%20Deep%20Context%20Interest%20Network%20for%20Click-Through%20Rate%20Prediction.pdf) <br />
* [2023 (Pinterest) (KDD) TransAct - Transformer-based Realtime User Action Model for Recommendation at Pinterest](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Sequence-Modeling/2023%20%28Pinterest%29%20%28KDD%29%20TransAct%20-%20Transformer-based%20Realtime%20User%20Action%20Model%20for%20Recommendation%20at%20Pinterest.pdf) <br />
* [2024 (Meta) (Arxiv) ** [GR] Actions Speak Louder than Words - Trillion-Parameter Sequential Transducers for Generative Recommendations](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Sequence-Modeling/2024%20%28Meta%29%20%28Arxiv%29%20%2A%2A%20%5BGR%5D%20Actions%20Speak%20Louder%20than%20Words%20-%20Trillion-Parameter%20Sequential%20Transducers%20for%20Generative%20Recommendations.pdf) <br />
* [2025 (Alibaba) (Arxiv) [LREA] Efficient Long Sequential Low-rank Adaptive Attention for Click-through rate Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Sequence-Modeling/2025%20%28Alibaba%29%20%28Arxiv%29%20%5BLREA%5D%20Efficient%20Long%20Sequential%20Low-rank%20Adaptive%20Attention%20for%20Click-through%20rate%20Prediction.pdf) <br />
* [2025 (Bytedance) ** (Arxiv) [LONGER] LONGER - Scaling Up Long Sequence Modeling in Industrial Recommenders](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Sequence-Modeling/2025%20%28Bytedance%29%20%2A%2A%20%28Arxiv%29%20%5BLONGER%5D%20LONGER%20-%20Scaling%20Up%20Long%20Sequence%20Modeling%20in%20Industrial%20Recommenders.pdf) <br />
* [2025 (Kuaishou) (SIGIR) [FIM] FIM - Frequency-Aware Multi-View Interest Modeling for Local-Life Service Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Sequence-Modeling/2025%20%28Kuaishou%29%20%28SIGIR%29%20%5BFIM%5D%20FIM%20-%20Frequency-Aware%20Multi-View%20Interest%20Modeling%20for%20Local-Life%20Service%20Recommendation.pdf) <br />
* [2025 （Bytedance) ** (Arxiv) [STCA] Make It Long, Keep It Fast - End-to-End 10k-Sequence Modeling at Billion Scale on Douyin](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Sequence-Modeling/2025%20%EF%BC%88Bytedance%29%20%2A%2A%20%28Arxiv%29%20%5BSTCA%5D%20Make%20It%20Long%2C%20Keep%20It%20Fast%20-%20End-to-End%2010k-Sequence%20Modeling%20at%20Billion%20Scale%20on%20Douyin.pdf) <br />
* [2026 (Bytedance) (Arxiv) [TM20K] Teacher Retains Full Tokens, Student Merges Efficiently - TM20K for E-Commerce Sequence Modeling in Ad Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Sequence-Modeling/2026%20%28Bytedance%29%20%28Arxiv%29%20%5BTM20K%5D%20Teacher%20Retains%20Full%20Tokens%2C%20Student%20Merges%20Efficiently%20-%20TM20K%20for%20E-Commerce%20Sequence%20Modeling%20in%20Ad%20Recommendation.pdf) <br />
* [2026 (Meta) (Arxiv) [LLaTTE] LLaTTE - Scaling Laws for Multi-Stage Sequence Modeling in Large-Scale Ads Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Sequence-Modeling/2026%20%28Meta%29%20%28Arxiv%29%20%5BLLaTTE%5D%20LLaTTE%20-%20Scaling%20Laws%20for%20Multi-Stage%20Sequence%20Modeling%20in%20Large-Scale%20Ads%20Recommendation.pdf) <br />
* [2026 (Meta) (Arxiv) [ULTRA-HSTU] Bending the Scaling Law Curve in Large-Scale Recommendation Systems](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Sequence-Modeling/2026%20%28Meta%29%20%28Arxiv%29%20%5BULTRA-HSTU%5D%20Bending%20the%20Scaling%20Law%20Curve%20in%20Large-Scale%20Recommendation%20Systems.pdf) <br />
* [2026 (Meta) Versioned Late Materialization for Ultra-Long Sequence Training in Recommendation Systems at Scale](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Sequence-Modeling/2026%20%28Meta%29%20Versioned%20Late%20Materialization%20for%20Ultra-Long%20Sequence%20Training%20in%20Recommendation%20Systems%20at%20Scale.pdf) <br />
* [2026 （Bytedance) (Arxiv) [SequenceO1] SequenceO1 - End-to-End Ultra-Long (100K) Sequence Modeling in Recommendation with Low-Rank Caching](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Sequence-Modeling/2026%20%EF%BC%88Bytedance%29%20%28Arxiv%29%20%5BSequenceO1%5D%20SequenceO1%20-%20End-to-End%20Ultra-Long%20%28100K%29%20Sequence%20Modeling%20in%20Recommendation%20with%20Low-Rank%20Caching.pdf) <br />

#### Transfer_Learning
* [2014 (Google) (NIPS) [Knoledge Distillation] Distilling the Knowledge in a Neural Network](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Transfer_Learning/2014%20%28Google%29%20%28NIPS%29%20%5BKnoledge%20Distillation%5D%20Distilling%20the%20Knowledge%20in%20a%20Neural%20Network.pdf) <br />
* [2015 (ICLR) [Fitnets] Fitnets - Hints for thin deep nets](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Transfer_Learning/2015%20%28ICLR%29%20%5BFitnets%5D%20Fitnets%20-%20Hints%20for%20thin%20deep%20nets.pdf) <br />
* [2018 (Alibaba) (AAAI) [Rocket] Rocket launching - A universal and efficient framework for training well-performing light net](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Transfer_Learning/2018%20%28Alibaba%29%20%28AAAI%29%20%5BRocket%5D%20Rocket%20launching%20-%20A%20universal%20and%20efficient%20framework%20for%20training%20well-performing%20light%20net.pdf) <br />
* [2018 (KDD)[Ranking Distillation] Ranking distillation - Learning compact ranking models with high performance for recommender system](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Transfer_Learning/2018%20%28KDD%29%5BRanking%20Distillation%5D%20Ranking%20distillation%20-%20Learning%20compact%20ranking%20models%20with%20high%20performance%20for%20recommender%20system.pdf) <br />
* [](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Transfer_Learning/Cross-domain) <br />
* [](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Transfer_Learning/Transfer) <br />

#### Trigger
* [2022 (Alibaba) (WWW) Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/04_Ranking/Trigger/2022%20%28Alibaba%29%20%28WWW%29%20Deep%20Interest%20Highlight%20Network%20for%20Click-Through%20Rate%20Prediction%20in%20Trigger-Induced%20Recommendation.pdf) <br />

## 05_Post-ranking
* [1998 (SIGIR) **  [MRR] The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/05_Post-ranking/1998%20%28SIGIR%29%20%2A%2A%20%20%5BMRR%5D%20The%20Use%20of%20MMR%2C%20Diversity-Based%20Reranking%20for%20Reordering%20Documents%20and%20Producing%20Summaries.pdf) <br />
* [2005 (WWW) Improving Recommendation Lists Through Topic Diversification](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/05_Post-ranking/2005%20%28WWW%29%20Improving%20Recommendation%20Lists%20Through%20Topic%20Diversification.pdf) <br />
* [2008 (SIGIR) [α-NDCG] Novelty and Diversity in Information Retrieval Evaluation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/05_Post-ranking/2008%20%28SIGIR%29%20%5B%CE%B1-NDCG%5D%20Novelty%20and%20Diversity%20in%20Information%20Retrieval%20Evaluation.pdf) <br />
* [2009 (Microsoft) (WSDM) Diversifying Search Results](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/05_Post-ranking/2009%20%28Microsoft%29%20%28WSDM%29%20Diversifying%20Search%20Results.pdf) <br />
* [2010 (WWW) Exploiting Query Reformulations for Web Search Result Diversification](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/05_Post-ranking/2010%20%28WWW%29%20Exploiting%20Query%20Reformulations%20for%20Web%20Search%20Result%20Diversification.pdf) <br />
* [2016 (Amazon) (RecSys) Adaptive, Personalized Diversity for Visual Discovery](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/05_Post-ranking/2016%20%28Amazon%29%20%28RecSys%29%20Adaptive%2C%20Personalized%20Diversity%20for%20Visual%20Discovery.pdf) <br />
* [2017 (Hulu) (NIPS) [DPP] Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/05_Post-ranking/2017%20%28Hulu%29%20%28NIPS%29%20%5BDPP%5D%20Fast%20Greedy%20MAP%20Inference%20for%20Determinantal%20Point%20Process%20to%20Improve%20Recommendation%20Diversity.pdf) <br />
* [2018 (Alibaba) (IJCAI) [Alibaba GMV] Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/05_Post-ranking/2018%20%28Alibaba%29%20%28IJCAI%29%20%5BAlibaba%20GMV%5D%20Globally%20Optimized%20Mutual%20Influence%20Aware%20Ranking%20in%20E-Commerce%20Search.pdf) <br />
* [2018 (Google) (CIKM) [DPP] Practical Diversified Recommendations on YouTube with Determinantal Point Processes](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/05_Post-ranking/2018%20%28Google%29%20%28CIKM%29%20%5BDPP%5D%20Practical%20Diversified%20Recommendations%20on%20YouTube%20with%20Determinantal%20Point%20Processes.pdf) <br />
* [2018 (SIGIR) [DLCM] Learning a Deep Listwise Context Model for Ranking Refinement](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/05_Post-ranking/2018%20%28SIGIR%29%20%5BDLCM%5D%20Learning%20a%20Deep%20Listwise%20Context%20Model%20for%20Ranking%20Refinement.pdf) <br />
* [2019  (Alibaba) (WWW) [Value-based RL] Value-aware Recommendation based on Reinforcement Profit Maximization](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/05_Post-ranking/2019%20%20%28Alibaba%29%20%28WWW%29%20%5BValue-based%20RL%5D%20Value-aware%20Recommendation%20based%20on%20Reinforcement%20Profit%20Maximization.pdf) <br />
* [2019 (Alibaba) (KDD) [GAttN] Exact-K Recommendation via Maximal Clique Optimization](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/05_Post-ranking/2019%20%28Alibaba%29%20%28KDD%29%20%5BGAttN%5D%20Exact-K%20Recommendation%20via%20Maximal%20Clique%20Optimization.pdf) <br />
* [2019 (Alibaba) (RecSys)  ** [PRM] Personalized Re-ranking for Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/05_Post-ranking/2019%20%28Alibaba%29%20%28RecSys%29%20%20%2A%2A%20%5BPRM%5D%20Personalized%20Re-ranking%20for%20Recommendation.pdf) <br />
* [2019 (Google) (Arxiv) Reinforcement Learning for Slate-based Recommender Systems - A Tractable Decomposition and Practical Methodology](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/05_Post-ranking/2019%20%28Google%29%20%28Arxiv%29%20Reinforcement%20Learning%20for%20Slate-based%20Recommender%20Systems%20-%20A%20Tractable%20Decomposition%20and%20Practical%20Methodology.pdf) <br />
* [2019 (Google) (Arxiv) Seq2slate - Re-ranking and slate optimization with rnns](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/05_Post-ranking/2019%20%28Google%29%20%28Arxiv%29%20Seq2slate%20-%20Re-ranking%20and%20slate%20optimization%20with%20rnns.pdf) <br />
* [2019 (Google) (IJCAI) [SlateQ] SLATEQ - A Tractable Decomposition for Reinforcement Learning with Recommendation Sets](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/05_Post-ranking/2019%20%28Google%29%20%28IJCAI%29%20%5BSlateQ%5D%20SLATEQ%20-%20A%20Tractable%20Decomposition%20for%20Reinforcement%20Learning%20with%20Recommendation%20Sets.pdf) <br />
* [2020 (Airbnb) (KDD) Managing Diversity in Airbnb Search](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/05_Post-ranking/2020%20%28Airbnb%29%20%28KDD%29%20Managing%20Diversity%20in%20Airbnb%20Search.pdf) <br />
* [2020 (Alibaba) (CIKM) [EdgeRec] EdgeRec - Recommender System on Edge in Mobile Taobao](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/05_Post-ranking/2020%20%28Alibaba%29%20%28CIKM%29%20%5BEdgeRec%5D%20EdgeRec%20-%20Recommender%20System%20on%20Edge%20in%20Mobile%20Taobao.pdf) <br />
* [2020 (Huawei) (Arxiv) Personalized Re-ranking for Improving Diversity in Live Recommender Systems](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/05_Post-ranking/2020%20%28Huawei%29%20%28Arxiv%29%20Personalized%20Re-ranking%20for%20Improving%20Diversity%20in%20Live%20Recommender%20Systems.pdf) <br />
* [2021 (Alibaba) (Arxiv) **  [PRS] Revisit Recommender System in the Permutation Prospective](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/05_Post-ranking/2021%20%28Alibaba%29%20%28Arxiv%29%20%2A%2A%20%20%5BPRS%5D%20Revisit%20Recommender%20System%20in%20the%20Permutation%20Prospective.pdf) <br />
* [2021 (Google) (WSDM) User Response Models to Improve a REINFORCE Recommender System](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/05_Post-ranking/2021%20%28Google%29%20%28WSDM%29%20User%20Response%20Models%20to%20Improve%20a%20REINFORCE%20Recommender%20System.pdf) <br />
* [2021 (Microsoft) Diversity on the Go! Streaming Determinantal Point Processes under a Maximum Induced Cardinality Objective](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/05_Post-ranking/2021%20%28Microsoft%29%20Diversity%20on%20the%20Go%21%20Streaming%20Determinantal%20Point%20Processes%20under%20a%20Maximum%20Induced%20Cardinality%20Objective.pdf) <br />
* [2023 (Amazon) (KDD) RankFormer - Listwise Learning-to-Rank Using Listwide Labels](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/05_Post-ranking/2023%20%28Amazon%29%20%28KDD%29%20RankFormer%20-%20Listwise%20Learning-to-Rank%20Using%20Listwide%20Labels.pdf) <br />
* [2023 (Meituan) (KDD) PIER - Permutation-Level Interest-Based End-to-End Re-ranking Framework in E-commerce](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/05_Post-ranking/2023%20%28Meituan%29%20%28KDD%29%20PIER%20-%20Permutation-Level%20Interest-Based%20End-to-End%20Re-ranking%20Framework%20in%20E-commerce.pdf) <br />
* [2024 (Kuaishou) (KDD) [NAR4Rec] Non-autoregressive Generative Models for Reranking Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/05_Post-ranking/2024%20%28Kuaishou%29%20%28KDD%29%20%5BNAR4Rec%5D%20Non-autoregressive%20Generative%20Models%20for%20Reranking%20Recommendation.pdf) <br />
* [2025 (Alibaba) (SIGIR) [SORT-Gen] A Generative Re-ranking Model for List-level Multi-objective Optimization at Taobao](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/05_Post-ranking/2025%20%28Alibaba%29%20%28SIGIR%29%20%5BSORT-Gen%5D%20A%20Generative%20Re-ranking%20Model%20for%20List-level%20Multi-objective%20Optimization%20at%20Taobao.pdf) <br />

#### Seq2Slate
* [2015 (Google) (Arxiv)  Deep Reinforcement Learning in Large Discrete Action Spaces ](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/05_Post-ranking/Seq2Slate/2015%20%28Google%29%20%28Arxiv%29%20%20Deep%20Reinforcement%20Learning%20in%20Large%20Discrete%20Action%20Spaces%20.pdf) <br />
* [2015 (Google) (Arxiv) Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/05_Post-ranking/Seq2Slate/2015%20%28Google%29%20%28Arxiv%29%20Deep%20Reinforcement%20Learning%20with%20Attention%20for%20Slate%20Markov%20Decision%20Processes%20with%20High-Dimensional%20States%20and%20Actions.pdf) <br />
* [2017 (KDD) [DCM] Deep Choice Model Using Pointer Networks for Airline Itinerary Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/05_Post-ranking/Seq2Slate/2017%20%28KDD%29%20%5BDCM%5D%20Deep%20Choice%20Model%20Using%20Pointer%20Networks%20for%20Airline%20Itinerary%20Prediction.pdf) <br />
* [2018 (Microsoft) (EMNLP) [RL4NMT] A study of reinforcement learning for neural machine translation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/05_Post-ranking/Seq2Slate/2018%20%28Microsoft%29%20%28EMNLP%29%20%5BRL4NMT%5D%20A%20study%20of%20reinforcement%20learning%20for%20neural%20machine%20translation.pdf) <br />
* [2019 (Google) (Arxiv) Seq2slate - Re-ranking and slate optimization with rnns](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/05_Post-ranking/Seq2Slate/2019%20%28Google%29%20%28Arxiv%29%20Seq2slate%20-%20Re-ranking%20and%20slate%20optimization%20with%20rnns.pdf) <br />

## 06_Relevance-ranking
* [2013 (Microsoft) (CIKM) [DSSM] Learning deep structured semantic models for web search using clickthrough data](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/06_Relevance-ranking/2013%20%28Microsoft%29%20%28CIKM%29%20%5BDSSM%5D%20Learning%20deep%20structured%20semantic%20models%20for%20web%20search%20using%20clickthrough%20data.pdf) <br />
* [2016 (Yahoo) (KDD) Ranking Relevance in Yahoo Search](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/06_Relevance-ranking/2016%20%28Yahoo%29%20%28KDD%29%20Ranking%20Relevance%20in%20Yahoo%20Search.pdf) <br />
* [2020 (ICLR) [StructBERT] StructBERT - Incorporating Language Structures into Pre-training for Deep Language Understanding](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/06_Relevance-ranking/2020%20%28ICLR%29%20%5BStructBERT%5D%20StructBERT%20-%20Incorporating%20Language%20Structures%20into%20Pre-training%20for%20Deep%20Language%20Understanding.pdf) <br />
* [2021 (Alibaba) (WWW) [MASM] Learning a Product Relevance Model from Click-Through Data in E-Commerce](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/06_Relevance-ranking/2021%20%28Alibaba%29%20%28WWW%29%20%5BMASM%5D%20Learning%20a%20Product%20Relevance%20Model%20from%20Click-Through%20Data%20in%20E-Commerce.pdf) <br />
* [2022 (Alibaba) (KDD) [ReprBERT] ReprBERT - Distilling BERT to an Efficient Representation-Based Relevance Model for E-Commerce](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/06_Relevance-ranking/2022%20%28Alibaba%29%20%28KDD%29%20%5BReprBERT%5D%20ReprBERT%20-%20Distilling%20BERT%20to%20an%20Efficient%20Representation-Based%20Relevance%20Model%20for%20E-Commerce.pdf) <br />
* [2023 (Meituan) (CIKM) [SPM] SPM - Structured Pretraining and Matching Architectures for Relevance Modeling in Meituan Search](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/06_Relevance-ranking/2023%20%28Meituan%29%20%28CIKM%29%20%5BSPM%5D%20SPM%20-%20Structured%20Pretraining%20and%20Matching%20Architectures%20for%20Relevance%20Modeling%20in%20Meituan%20Search.pdf) <br />
* [2024 (Alibaba) (KDD) [DeepBoW] Deep Bag-of-Words Model - An Efficient and Interpretable Relevance Architecture for Chinese E-Commerce](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/06_Relevance-ranking/2024%20%28Alibaba%29%20%28KDD%29%20%5BDeepBoW%5D%20Deep%20Bag-of-Words%20Model%20-%20An%20Efficient%20and%20Interpretable%20Relevance%20Architecture%20for%20Chinese%20E-Commerce.pdf) <br />
* [2024 （Walmart) (SIGIR) Large Language Models for Relevance Judgment in Product Search](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/06_Relevance-ranking/2024%20%EF%BC%88Walmart%29%20%28SIGIR%29%20Large%20Language%20Models%20for%20Relevance%20Judgment%20in%20Product%20Search.pdf) <br />
* [2025 (Alibaba) (Arxiv) LORE - A Large Generative Model for Search Relevance](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/06_Relevance-ranking/2025%20%28Alibaba%29%20%28Arxiv%29%20LORE%20-%20A%20Large%20Generative%20Model%20for%20Search%20Relevance.pdf) <br />
* [2025 (Alibaba) (WWW) [ELLM] Explainable LLM-driven Multi-dimensional Distillation for E-Commerce Relevance Learning](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/06_Relevance-ranking/2025%20%28Alibaba%29%20%28WWW%29%20%5BELLM%5D%20Explainable%20LLM-driven%20Multi-dimensional%20Distillation%20for%20E-Commerce%20Relevance%20Learning.pdf) <br />
* [2025 (Kuaishou) (Arxiv) [HCMRM] HCMRM -A High-Consistency Multimodal Relevance Model for Search Ads](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/06_Relevance-ranking/2025%20%28Kuaishou%29%20%28Arxiv%29%20%5BHCMRM%5D%20HCMRM%20-A%20High-Consistency%20Multimodal%20Relevance%20Model%20for%20Search%20Ads.pdf) <br />
* [2025 (Linkedin) (Arxiv) Powering Job Search at Scale - LLM-Enhanced Query Understanding in Job Matching Systems](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/06_Relevance-ranking/2025%20%28Linkedin%29%20%28Arxiv%29%20Powering%20Job%20Search%20at%20Scale%20-%20LLM-Enhanced%20Query%20Understanding%20in%20Job%20Matching%20Systems.pdf) <br />
* [2025 （Alibaba) (Arxiv) TaoSR1 - The Thinking Model for E-commerce Relevance Search](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/06_Relevance-ranking/2025%20%EF%BC%88Alibaba%29%20%28Arxiv%29%20TaoSR1%20-%20The%20Thinking%20Model%20for%20E-commerce%20Relevance%20Search.pdf) <br />
* [2025 （Tencent) (KDD) [GenFR] Applying Large Language Model For Relevance Search In Tencent](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/06_Relevance-ranking/2025%20%EF%BC%88Tencent%29%20%28KDD%29%20%5BGenFR%5D%20Applying%20Large%20Language%20Model%20For%20Relevance%20Search%20In%20Tencent.pdf) <br />

## 07_LLM


#### 01_LLM_Classical
* [2013 (Google) (NIPS) [Word2vec] Distributed Representations of Words and Phrases and their Compositionality](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/2013%20%28Google%29%20%28NIPS%29%20%5BWord2vec%5D%20Distributed%20Representations%20of%20Words%20and%20Phrases%20and%20their%20Compositionality.pdf) <br />
* [2014 (Google) (NIPS) [Seq2Seq] Sequence to Sequence Learning with Neural Networks](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/2014%20%28Google%29%20%28NIPS%29%20%5BSeq2Seq%5D%20Sequence%20to%20Sequence%20Learning%20with%20Neural%20Networks.pdf) <br />
* [2017 (Google) (NIPS) [Transformer] Attention Is All You Need](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/2017%20%28Google%29%20%28NIPS%29%20%5BTransformer%5D%20Attention%20Is%20All%20You%20Need.pdf) <br />
* [2017 (OpenAI) (NIPS) [RLHF] Deep Reinforcement Learning from Human Preferences](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/2017%20%28OpenAI%29%20%28NIPS%29%20%5BRLHF%5D%20Deep%20Reinforcement%20Learning%20from%20Human%20Preferences.pdf) <br />
* [2018 (OpenAI) (Arxiv) [GPT-1] Improving Language Understanding by Generative Pre-Training](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/2018%20%28OpenAI%29%20%28Arxiv%29%20%5BGPT-1%5D%20Improving%20Language%20Understanding%20by%20Generative%20Pre-Training.pdf) <br />
* [2019 (Google) (NAACL) [Bert] BERT - Pre-training of Deep Bidirectional Transformers for Language Understanding](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/2019%20%28Google%29%20%28NAACL%29%20%5BBert%5D%20BERT%20-%20Pre-training%20of%20Deep%20Bidirectional%20Transformers%20for%20Language%20Understanding.pdf) <br />
* [2019 (OpenAI) (Arxiv) [GPT-2] Language Models are Unsupervised Multitask Learners](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/2019%20%28OpenAI%29%20%28Arxiv%29%20%5BGPT-2%5D%20Language%20Models%20are%20Unsupervised%20Multitask%20Learners.pdf) <br />
* [2020 (Arxiv) Scaling Laws for Neural Language Models](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/2020%20%28Arxiv%29%20Scaling%20Laws%20for%20Neural%20Language%20Models.pdf) <br />
* [2020 (Meta) (NIPS) [RAG] Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/2020%20%28Meta%29%20%28NIPS%29%20%5BRAG%5D%20Retrieval-Augmented%20Generation%20for%20Knowledge-Intensive%20NLP%20Tasks.pdf) <br />
* [2020 (OpenAI) (Arxiv) [GPT-3] Language Models are Few-Shot Learners](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/2020%20%28OpenAI%29%20%28Arxiv%29%20%5BGPT-3%5D%20Language%20Models%20are%20Few-Shot%20Learners.pdf) <br />
* [2021 (Microsoft) (Arxiv) [LoRA] LoRA - Low-Rank Adaptation of Large Language Models](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/2021%20%28Microsoft%29%20%28Arxiv%29%20%5BLoRA%5D%20LoRA%20-%20Low-Rank%20Adaptation%20of%20Large%20Language%20Models.pdf) <br />
* [2022 (Google) (Arxiv) [PaLM] PaLM - Scaling Language Modeling with Pathways](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/2022%20%28Google%29%20%28Arxiv%29%20%5BPaLM%5D%20PaLM%20-%20Scaling%20Language%20Modeling%20with%20Pathways.pdf) <br />
* [2022 (Google) (JMLR) [SwitchTransfomers] Switch Transformers - Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/2022%20%28Google%29%20%28JMLR%29%20%5BSwitchTransfomers%5D%20Switch%20Transformers%20-%20Scaling%20to%20Trillion%20Parameter%20Models%20with%20Simple%20and%20Efficient%20Sparsity.pdf) <br />
* [2022 (Google) (NIPS) [COT] Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/2022%20%28Google%29%20%28NIPS%29%20%5BCOT%5D%20Chain-of-Thought%20Prompting%20Elicits%20Reasoning%20in%20Large%20Language%20Models.pdf) <br />
* [2022 (Google) (NIPS) [ChainOfThought] Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/2022%20%28Google%29%20%28NIPS%29%20%5BChainOfThought%5D%20Chain-of-Thought%20Prompting%20Elicits%20Reasoning%20in%20Large%20Language%20Models.pdf) <br />
* [2022 (Google) (TMLR) [Emergent] Emergent Abilities of Large Language Models](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/2022%20%28Google%29%20%28TMLR%29%20%5BEmergent%5D%20Emergent%20Abilities%20of%20Large%20Language%20Models.pdf) <br />
* [2022 (OpenAI) (Arxiv) [InstructGPT] [RLHF] Training language models to follow instructions with human feedback](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/2022%20%28OpenAI%29%20%28Arxiv%29%20%5BInstructGPT%5D%20%5BRLHF%5D%20Training%20language%20models%20to%20follow%20instructions%20with%20human%20feedback.pdf) <br />
* [2022 (OpenAI) (Arxiv) [PPO] Proximal Policy Optimization Algorithms](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/2022%20%28OpenAI%29%20%28Arxiv%29%20%5BPPO%5D%20Proximal%20Policy%20Optimization%20Algorithms.pdf) <br />
* [2022 (OpenAI) (Arxiv) [WebGPT] Learning to summarize from human feedback](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/2022%20%28OpenAI%29%20%28Arxiv%29%20%5BWebGPT%5D%20Learning%20to%20summarize%20from%20human%20feedback.pdf) <br />
* [2022 (OpenAI) (Arxiv) [WebGPT] WebGPT - Browser-assisted question-answering with human feedback](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/2022%20%28OpenAI%29%20%28Arxiv%29%20%5BWebGPT%5D%20WebGPT%20-%20Browser-assisted%20question-answering%20with%20human%20feedback.pdf) <br />
* [2023 (Alibaba) (Arxiv) [QWEN] QWEN Technical Report](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/2023%20%28Alibaba%29%20%28Arxiv%29%20%5BQWEN%5D%20QWEN%20Technical%20Report.pdf) <br />
* [2023 (Meta) (Arxiv) [LLaMA-2] Llama 2 - Open Foundation and Fine-Tuned ChatModels](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/2023%20%28Meta%29%20%28Arxiv%29%20%5BLLaMA-2%5D%20Llama%202%20-%20Open%20Foundation%20and%20Fine-Tuned%20ChatModels.pdf) <br />
* [2023 (Meta) (Arxiv) [LLaMA] LLaMA - Open and Efficient Foundation Language Models](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/2023%20%28Meta%29%20%28Arxiv%29%20%5BLLaMA%5D%20LLaMA%20-%20Open%20and%20Efficient%20Foundation%20Language%20Models.pdf) <br />
* [2023 (OpenAI) (Arxiv) [GPT4] GPT-4 Technical Report](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/2023%20%28OpenAI%29%20%28Arxiv%29%20%5BGPT4%5D%20GPT-4%20Technical%20Report.pdf) <br />
* [2024 (Alibaba) (Arxiv) [QWEN2] QWEN2 Technical Report](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/2024%20%28Alibaba%29%20%28Arxiv%29%20%5BQWEN2%5D%20QWEN2%20Technical%20Report.pdf) <br />
* [2024 (Arxiv) [TinyLlama] Arxiv TinyLlama - An Open-Source Small Language Model](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/2024%20%28Arxiv%29%20%5BTinyLlama%5D%20Arxiv%20TinyLlama%20-%20An%20Open-Source%20Small%20Language%20Model.pdf) <br />
* [2024 (DeepSeek) (Arxiv) [GRPO] DeepSeekMath - Pushing the Limits of Mathematical Reasoning in Open Language Models](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/2024%20%28DeepSeek%29%20%28Arxiv%29%20%5BGRPO%5D%20DeepSeekMath%20-%20Pushing%20the%20Limits%20of%20Mathematical%20Reasoning%20in%20Open%20Language%20Models.pdf) <br />
* [2025 (Alibaba) (Arxiv) [QWEN-2.5] QWEN 2.5 Technical Report](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/2025%20%28Alibaba%29%20%28Arxiv%29%20%5BQWEN-2.5%5D%20QWEN%202.5%20Technical%20Report.pdf) <br />
* [2025 (Alibaba) (Arxiv) [Qwen2.5-VL] Qwen2.5-VL Technical Report](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/2025%20%28Alibaba%29%20%28Arxiv%29%20%5BQwen2.5-VL%5D%20Qwen2.5-VL%20Technical%20Report.pdf) <br />
* [2025 (Alibaba) (Arxiv) [Qwen3 Embedding] Qwen3 Embedding - Advancing Text Embedding and Reranking Through Foundation Models](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/2025%20%28Alibaba%29%20%28Arxiv%29%20%5BQwen3%20Embedding%5D%20Qwen3%20Embedding%20-%20Advancing%20Text%20Embedding%20and%20Reranking%20Through%20Foundation%20Models.pdf) <br />
* [2025 (Alibaba) (Arxiv) [Qwen3] Qwen3 Technical Report](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/2025%20%28Alibaba%29%20%28Arxiv%29%20%5BQwen3%5D%20Qwen3%20Technical%20Report.pdf) <br />
* [2025 (Arxiv) A Survey of Large Language Models](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/2025%20%28Arxiv%29%20A%20Survey%20of%20Large%20Language%20Models.pdf) <br />
* [2025 (DeepSeek) (Nature) [DeepSeek-R1] DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/2025%20%28DeepSeek%29%20%28Nature%29%20%5BDeepSeek-R1%5D%20DeepSeek-R1%20incentivizes%20reasoning%20in%20LLMs%20through%20reinforcement%20learning.pdf) <br />
* [2025 (DeepSeek) [DeepSeek-R1] DeepSeek-R1 -Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/2025%20%28DeepSeek%29%20%5BDeepSeek-R1%5D%20DeepSeek-R1%20-Incentivizing%20Reasoning%20Capability%20in%20LLMs%20via%20Reinforcement%20Learning.pdf) <br />
* [2025 (DeepSeek) [DeepSeek-V3] DeepSeek-V3 Technical Report](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/2025%20%28DeepSeek%29%20%5BDeepSeek-V3%5D%20DeepSeek-V3%20Technical%20Report.pdf) <br />
* [2025 (Google) (Arxiv) [SigLIP2] SigLIP 2 - Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/2025%20%28Google%29%20%28Arxiv%29%20%5BSigLIP2%5D%20SigLIP%202%20-%20Multilingual%20Vision-Language%20Encoders%20with%20Improved%20Semantic%20Understanding%2C%20Localization%2C%20and%20Dense%20Features.pdf) <br />
* [](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/Book) <br />
* [](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/MOE) <br />
* [](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/ModelOptimization) <br />
* [](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/MultiModal) <br />
* [](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/Quant) <br />
* [](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/SFT) <br />
* [](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/SpecificApplication) <br />
* [](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/Survey) <br />
* [resources](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/01_LLM_Classical/resources.txt) <br />

#### 02_Self_Supervised_Learning
* [2020 (Alibaba) (AAAI) [DMR] Deep Match to Rank Model for Personalized Click-Through Rate Prediction](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/02_Self_Supervised_Learning/2020%20%28Alibaba%29%20%28AAAI%29%20%5BDMR%5D%20Deep%20Match%20to%20Rank%20Model%20for%20Personalized%20Click-Through%20Rate%20Prediction.pdf) <br />
* [2020 (Alibaba) (CIKM) [BERT4Rec] BERT4Rec - Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/02_Self_Supervised_Learning/2020%20%28Alibaba%29%20%28CIKM%29%20%5BBERT4Rec%5D%20BERT4Rec%20-%20Sequential%20Recommendation%20with%20Bidirectional%20Encoder%20Representations%20from%20Transformer.pdf) <br />
* [2020 (Alibaba) (KDD) Disentangled Self-Supervision in Sequential Recommenders](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/02_Self_Supervised_Learning/2020%20%28Alibaba%29%20%28KDD%29%20Disentangled%20Self-Supervision%20in%20Sequential%20Recommenders.pdf) <br />
* [2020 (Arxiv) UserBERT - Self-supervised User Representation Learning](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/02_Self_Supervised_Learning/2020%20%28Arxiv%29%20UserBERT%20-%20Self-supervised%20User%20Representation%20Learning.pdf) <br />
* [2020 (Arxiv) [SGL] Self-supervised Graph Learning for Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/02_Self_Supervised_Learning/2020%20%28Arxiv%29%20%5BSGL%5D%20Self-supervised%20Graph%20Learning%20for%20Recommendation.pdf) <br />
* [2020 (CIKM) [S3Rec] S3-Rec - Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/02_Self_Supervised_Learning/2020%20%28CIKM%29%20%5BS3Rec%5D%20S3-Rec%20-%20Self-Supervised%20Learning%20for%20Sequential%20Recommendation%20with%20Mutual%20Information%20Maximization.pdf) <br />
* [2020 (EMNLP) [PTUM] PTUM - Pre-training User Model from Unlabeled User Behaviors via Self-supervision](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/02_Self_Supervised_Learning/2020%20%28EMNLP%29%20%5BPTUM%5D%20PTUM%20-%20Pre-training%20User%20Model%20from%20Unlabeled%20User%20Behaviors%20via%20Self-supervision.pdf) <br />
* [2020 (SIGIR) Self-Supervised Reinforcement Learning for Recommender Systems](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/02_Self_Supervised_Learning/2020%20%28SIGIR%29%20Self-Supervised%20Reinforcement%20Learning%20for%20Recommender%20Systems.pdf) <br />
* [2021 (Alibaba) (Arxiv) [CLRec] Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/02_Self_Supervised_Learning/2021%20%28Alibaba%29%20%28Arxiv%29%20%5BCLRec%5D%20Contrastive%20Learning%20for%20Debiased%20Candidate%20Generation%20in%20Large-Scale%20Recommender%20Systems.pdf) <br />
* [2021 (Alibaba) (CIKM) * [ZEUS] Self-Supervised Learning on Users’ Spontaneous Behaviors for Multi-Scenario Ranking in E-commerce](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/02_Self_Supervised_Learning/2021%20%28Alibaba%29%20%28CIKM%29%20%2A%20%5BZEUS%5D%20Self-Supervised%20Learning%20on%20Users%E2%80%99%20Spontaneous%20Behaviors%20for%20Multi-Scenario%20Ranking%20in%20E-commerce.pdf) <br />
* [2021 (Alibaba) (WWW) Contrastive Pre-training for Sequential Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/02_Self_Supervised_Learning/2021%20%28Alibaba%29%20%28WWW%29%20Contrastive%20Pre-training%20for%20Sequential%20Recommendation.pdf) <br />
* [2021 (Google) (CIKM) Self-supervised Learning for Large-scale Item Recommendations](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/02_Self_Supervised_Learning/2021%20%28Google%29%20%28CIKM%29%20Self-supervised%20Learning%20for%20Large-scale%20Item%20Recommendations.pdf) <br />
* [2021 (WSDM) [Prop] PROP - Pre-training with Representative Words Prediction for Ad-hoc Retrieval](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/07_LLM/02_Self_Supervised_Learning/2021%20%28WSDM%29%20%5BProp%5D%20PROP%20-%20Pre-training%20with%20Representative%20Words%20Prediction%20for%20Ad-hoc%20Retrieval.pdf) <br />

## 08_Deep_Learning
* [2012 (NIPS) [CNN] ImageNet Classification with Deep Convolutional Neural Networks](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/08_Deep_Learning/2012%20%28NIPS%29%20%5BCNN%5D%20ImageNet%20Classification%20with%20Deep%20Convolutional%20Neural%20Networks.pdf) <br />
* [2014 (JMLR) [Dropout] Dropout - A Simple Way to Prevent Neural Networks from Overfitting](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/08_Deep_Learning/2014%20%28JMLR%29%20%5BDropout%5D%20Dropout%20-%20A%20Simple%20Way%20to%20Prevent%20Neural%20Networks%20from%20Overfitting.pdf) <br />
* [2015 (Google) (JMLR) [BatchNorm] Batch Normalization - Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/08_Deep_Learning/2015%20%28Google%29%20%28JMLR%29%20%5BBatchNorm%5D%20Batch%20Normalization%20-%20Accelerating%20Deep%20Network%20Training%20by%20Reducing%20Internal%20Covariate%20Shift.pdf) <br />
* [2015 (OpenAI) (ICLR) [Adam] Adam - A Method for Stochastic Optimization](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/08_Deep_Learning/2015%20%28OpenAI%29%20%28ICLR%29%20%5BAdam%5D%20Adam%20-%20A%20Method%20for%20Stochastic%20Optimization.pdf) <br />
* [2016 (CVPR) [ResNet] Deep Residual Learning for Image Recognition](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/08_Deep_Learning/2016%20%28CVPR%29%20%5BResNet%5D%20Deep%20Residual%20Learning%20for%20Image%20Recognition.pdf) <br />
* [2016 (OpenAI) (NIPS) [Weight Norm] Weight Normalization - A Simple Reparameterization to Accelerate Training of Deep Neural Networks](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/08_Deep_Learning/2016%20%28OpenAI%29%20%28NIPS%29%20%5BWeight%20Norm%5D%20Weight%20Normalization%20-%20A%20Simple%20Reparameterization%20to%20Accelerate%20Training%20of%20Deep%20Neural%20Networks.pdf) <br />
* [2017 (Arxiv) [LayerNorm] Layer Normalization](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/08_Deep_Learning/2017%20%28Arxiv%29%20%5BLayerNorm%5D%20Layer%20Normalization.pdf) <br />
* [2017 (Google) (NIPS) [Transformer] Attention Is All You Need](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/08_Deep_Learning/2017%20%28Google%29%20%28NIPS%29%20%5BTransformer%5D%20Attention%20Is%20All%20You%20Need.pdf) <br />
* [2020 (Arxiv) GLU Variants Improve Transformer](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/08_Deep_Learning/2020%20%28Arxiv%29%20GLU%20Variants%20Improve%20Transformer.pdf) <br />
* [2020 (ICML) On Layer Normalization in the Transformer Architecture](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/08_Deep_Learning/2020%20%28ICML%29%20On%20Layer%20Normalization%20in%20the%20Transformer%20Architecture.pdf) <br />

## 09_Reinforcement_Learning
* [2010 (Yahoo) (WWW) [LinUCB] A Contextual-Bandit Approach to Personalized News Article Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/09_Reinforcement_Learning/2010%20%28Yahoo%29%20%28WWW%29%20%5BLinUCB%5D%20A%20Contextual-Bandit%20Approach%20to%20Personalized%20News%20Article%20Recommendation.pdf) <br />
* [2018 (Spotify) (Recsys) [Spotify Bandit] Explore, Exploit, and Explain Personalizing Explainable Recommendations with Bandits](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/09_Reinforcement_Learning/2018%20%28Spotify%29%20%28Recsys%29%20%5BSpotify%20Bandit%5D%20Explore%2C%20Exploit%2C%20and%20Explain%20Personalizing%20Explainable%20Recommendations%20with%20Bandits.pdf) <br />
* [2018 [Microsoft] (WWW) [DRN] DRN - A Deep Reinforcement Learning Framework for News Recommendation](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/09_Reinforcement_Learning/2018%20%5BMicrosoft%5D%20%28WWW%29%20%5BDRN%5D%20DRN%20-%20A%20Deep%20Reinforcement%20Learning%20Framework%20for%20News%20Recommendation.pdf) <br />
* [2019 (Google) (IJCAI) *[SlateQ] SLATEQ - A Tractable Decomposition for Reinforcement Learning with Recommendation Sets](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/09_Reinforcement_Learning/2019%20%28Google%29%20%28IJCAI%29%20%2A%5BSlateQ%5D%20SLATEQ%20-%20A%20Tractable%20Decomposition%20for%20Reinforcement%20Learning%20with%20Recommendation%20Sets.pdf) <br />
* [2019 (Google) (WSDM) *[Top-K Off-Policy] Top-K Off-Policy Correction for a REINFORCE Recommender System](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/09_Reinforcement_Learning/2019%20%28Google%29%20%28WSDM%29%20%2A%5BTop-K%20Off-Policy%5D%20Top-K%20Off-Policy%20Correction%20for%20a%20REINFORCE%20Recommender%20System.pdf) <br />
* [2019 (Sigweb) Deep Reinforcement Learning for Search, Recommendation, and Online Advertising - A Survey](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/09_Reinforcement_Learning/2019%20%28Sigweb%29%20Deep%20Reinforcement%20Learning%20for%20Search%2C%20Recommendation%2C%20and%20Online%20Advertising%20-%20A%20Survey.pdf) <br />
* [2020 (Bytedance) (KDD) [RAM] Jointly Learning to Recommend and Advertise](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/09_Reinforcement_Learning/2020%20%28Bytedance%29%20%28KDD%29%20%5BRAM%5D%20Jointly%20Learning%20to%20Recommend%20and%20Advertise.pdf) <br />
* [2020 (JD) (SIGIR) [NICF] Neural Interactive Collaborative Filtering](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/09_Reinforcement_Learning/2020%20%28JD%29%20%28SIGIR%29%20%5BNICF%5D%20Neural%20Interactive%20Collaborative%20Filtering.pdf) <br />

#### RL_classical
* [1992 (ML) [REINFORCE] Simple statistical gradient-following algorithms for connectionist reinforcement learning](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/09_Reinforcement_Learning/RL_classical/1992%20%28ML%29%20%5BREINFORCE%5D%20Simple%20statistical%20gradient-following%20algorithms%20for%20connectionist%20reinforcement%20learning.pdf) <br />
* [1999 (NIPS) [Actor-Critic] Actor-Critic Algorithms](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/09_Reinforcement_Learning/RL_classical/1999%20%28NIPS%29%20%5BActor-Critic%5D%20Actor-Critic%20Algorithms.pdf) <br />
* [2013 (DeepMind) (Arxiv) [DQN] Playing Atari with Deep Reinforcement Learning](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/09_Reinforcement_Learning/RL_classical/2013%20%28DeepMind%29%20%28Arxiv%29%20%5BDQN%5D%20Playing%20Atari%20with%20Deep%20Reinforcement%20Learning.pdf) <br />
* [2015 (DeepMind) (AAAI) [Double Q-learning] Deep Reinforcement Learning with Double Q-learning](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/09_Reinforcement_Learning/RL_classical/2015%20%28DeepMind%29%20%28AAAI%29%20%5BDouble%20Q-learning%5D%20Deep%20Reinforcement%20Learning%20with%20Double%20Q-learning.pdf) <br />
* [2015 (DeepMind) (Nature) [DQN] Human-level control through deep reinforcement learning](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/09_Reinforcement_Learning/RL_classical/2015%20%28DeepMind%29%20%28Nature%29%20%5BDQN%5D%20Human-level%20control%20through%20deep%20reinforcement%20learning.pdf) <br />
* [2016 (Google) (Arxiv) [A3C] Asynchronous Methods for Deep Reinforcement Learning](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/09_Reinforcement_Learning/RL_classical/2016%20%28Google%29%20%28Arxiv%29%20%5BA3C%5D%20Asynchronous%20Methods%20for%20Deep%20Reinforcement%20Learning.pdf) <br />
* [2016 (OpenAI) (Nature) Mastering the game of Go with deep neural networks and tree search](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/09_Reinforcement_Learning/RL_classical/2016%20%28OpenAI%29%20%28Nature%29%20Mastering%20the%20game%20of%20Go%20with%20deep%20neural%20networks%20and%20tree%20search.pdf) <br />
* [2017 (JMLR) [TRPO] Trust Region Policy Optimization](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/09_Reinforcement_Learning/RL_classical/2017%20%28JMLR%29%20%5BTRPO%5D%20Trust%20Region%20Policy%20Optimization.pdf) <br />
* [2017 (OpenAI) (Arxiv) [PPO] Proximal Policy Optimization Algorithms](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/09_Reinforcement_Learning/RL_classical/2017%20%28OpenAI%29%20%28Arxiv%29%20%5BPPO%5D%20Proximal%20Policy%20Optimization%20Algorithms.pdf) <br />
* [2017 （OpenAI) (Arxiv) [ES] Evolution Strategies as a Scalable Alternative to Reinforcement Learning](https://github.com/guyulongcs/Deep-Learning-for-Search-Recommendation-Advertisements/blob/master/09_Reinforcement_Learning/RL_classical/2017%20%EF%BC%88OpenAI%29%20%28Arxiv%29%20%5BES%5D%20Evolution%20Strategies%20as%20a%20Scalable%20Alternative%20to%20Reinforcement%20Learning.pdf) <br />