# 推荐系统相关论文汇总 ([English Version is Here](/README_EN.md)) ## 介绍 1. 截至2026-06-11，本仓库收集汇总了推荐系统领域相关论文共**948**篇，涉及：**召回**，**粗排**，**精排**，**重排**，**多任务**，**多场景**，**多模态**，**冷启动**，**校准**， **纠偏**，**多样性**，**公平性**，**反馈延迟**，**蒸馏**，**对比学习**，**因果推断**，**Look-Alike**，**Learning-to-Rank**，**强化学习**等领域，本仓库会跟踪业界进展，持续更新。 2. 因文件名特殊字符的限制，故论文title中所有的`:`都改为了`-`，检索时请注意。 3. 文件名前缀中带有`[]`的，表明本人已经通读过，第一个`[]`中为论文年份，第二个`[]`中为发表机构或公司(可选)，第三个`[]`中为论文提出的model或method的简称(可选)。 4. 在某些一级分类下面，还有若干二级分类；一篇论文可能应该涉及多个二级分类(例如用对比学习的方法做召回)，最终我会将论文放在较主要的那一类下；分类也会随时调整优化，欢迎在`issue`中提出宝贵意见。 5. 若您是文章作者，且不希望您的论文出现在这里，请在`issue`中提出，我核实后会马上下架。 6. 关于排序算法的一些实现，请见我的另一个repo: https://github.com/tangxyw/RecAlgorithm 7. 本仓库仅供交流学习使用，不做任何商业目的。 ## 联系方式 pic ## 论文目录 - [Rank](#Rank) - [Industry](#Industry) - [Pre-Rank](#Pre-Rank) - [Re-Rank](#Re-Rank) - [Match](#Match) - [Multi-Task](#Multi-Task) - [Multi-Modal](#Multi-Modal) - [Multi-Scenario](#Multi-Scenario) - [Debias](#Debias) - [Calibration](#Calibration) - [Distillation](#Distillation) - [Feedback-Delay](#Feedback-Delay) - [ContrastiveLearning](#ContrastiveLearning) - [Cold-Start](#Cold-Start) - [Learning-to-Rank](#Learning-to-Rank) - [Fairness](#Fairness) - [Look-Alike](#Look-Alike) - [CausalInference](#CausalInference) - [Diversity](#Diversity) - [ABTest](#ABTest) - [ReinforcementLearning](#ReinforcementLearning) ## Rank - [[2009][BPR] Bayesian Personalized Ranking from Implicit Feedback](Rank/%5B2009%5D%5BBPR%5D%20Bayesian%20Personalized%20Ranking%20from%20Implicit%20Feedback.pdf) - [[2010][FM] Factorization Machines](Rank/%5B2010%5D%5BFM%5D%20Factorization%20Machines.pdf) - [[2014][Facebook][GBDT+LR] Practical Lessons from Predicting Clicks on Ads at Facebook](Rank/%5B2014%5D%5BFacebook%5D%5BGBDT%2BLR%5D%20Practical%20Lessons%20from%20Predicting%20Clicks%20on%20Ads%20at%20Facebook.pdf) - [[2016][UCL][FNN] Deep Learning over Multi-field Categorical Data](Rank/%5B2016%5D%5BUCL%5D%5BFNN%5D%20Deep%20Learning%20over%20Multi-field%20Categorical%20Data.pdf) - [[2016][Microsft][Deep Crossing] Deep Crossing - Web-Scale Modeling without Manually Crafted Combinatorial Features](Rank/%5B2016%5D%5BMicrosft%5D%5BDeep%20Crossing%5D%20Deep%20Crossing%20-%20Web-Scale%20Modeling%20without%20Manually%20Crafted%20Combinatorial%20Features.pdf) - [[2016][Google][Wide&Deep] Wide & Deep Learning for Recommender Systems](Rank/%5B2016%5D%5BGoogle%5D%5BWide%26Deep%5D%20Wide%20%26%20Deep%20Learning%20for%20Recommender%20Systems.pdf) - [[2016][SJTU][PNN] Product-based Neural Networks for User Response Prediction](Rank/%5B2016%5D%5BSJTU%5D%5BPNN%5D%20Product-based%20Neural%20Networks%20for%20User%20Response%20Prediction.pdf) - [[2016][NTU][FFM] Field-aware Factorization Machines for CTR Prediction](Rank/%5B2016%5D%5BNTU%5D%5BFFM%5D%20Field-aware%20Factorization%20Machines%20for%20CTR%20Prediction.pdf) - [[2017][Stanford][DCN] Deep & Cross Network for Ad Click Predictions](Rank/%5B2017%5D%5BStanford%5D%5BDCN%5D%20Deep%20%26%20Cross%20Network%20for%20Ad%20Click%20Predictions.pdf) - [[2017][NUS][NFM] Neural Factorization Machines for Sparse Predictive Analytics](Rank/%5B2017%5D%5BNUS%5D%5BNFM%5D%20Neural%20Factorization%20Machines%20for%20Sparse%20Predictive%20Analytics.pdf) - [[2017][ZJU][AFM] Attentional Factorization Machines - Learning the Weight of Feature Interactions via Attention Networks](Rank/%5B2017%5D%5BZJU%5D%5BAFM%5D%20Attentional%20Factorization%20Machines%20-%20Learning%20the%20Weight%20of%20Feature%20Interactions%20via%20Attention%20Networks.pdf) - [[2017][NUS][NCF] Neural Collaborative Filtering](Rank/%5B2017%5D%5BNUS%5D%5BNCF%5D%20Neural%20Collaborative%20Filtering.pdf) - [[2017][Alibaba][MLR] Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction](Rank/%5B2017%5D%5BAlibaba%5D%5BMLR%5D%20Learning%20Piece-wise%20Linear%20Models%20from%20Large%20Scale%20Data%20for%20Ad%20Click%20Prediction.pdf) - [[2017][Huawei][DeepFM] A Factorization-Machine based Neural Network for CTR Prediction](Rank/%5B2017%5D%5BHuawei%5D%5BDeepFM%5D%20A%20Factorization-Machine%20based%20Neural%20Network%20for%20CTR%20Prediction.pdf) - [[2018][USTC][xDeepFM] xDeepFM - Combining Explicit and Implicit Feature Interactions for Recommender Systems](Rank/%5B2018%5D%5BUSTC%5D%5BxDeepFM%5D%20xDeepFM%20-%20Combining%20Explicit%20and%20Implicit%20Feature%20Interactions%20for%20Recommender%20Systems.pdf) - [[2019][AutoInt] AutoInt - Automatic Feature Interaction Learning via Self-Attentive Neural Networks](Rank/%5B2019%5D%5BAutoInt%5D%20AutoInt%20-%20Automatic%20Feature%20Interaction%20Learning%20via%20Self-Attentive%20Neural%20Networks.pdf) - [DCN V2 - Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](Rank/DCN%20V2%20-%20Improved%20Deep%20%26%20Cross%20Network%20and%20Practical%20Lessons%20for%20Web-scale%20Learning%20to%20Rank%20Systems.pdf) - [SESSION-BASED RECOMMENDATIONS WITH RECURRENT NEURAL NETWORKS](Rank/SESSION-BASED%20RECOMMENDATIONS%20WITH%20RECURRENT%20NEURAL%20NETWORKS.pdf) ## Industry - [[2016][Youtube] Deep Neural Networks for YouTube Recommendations](Industry/%5B2016%5D%5BYoutube%5D%20Deep%20Neural%20Networks%20for%20YouTube%20Recommendations.pdf) - [[2016][Microsoft] User Fatigue in Online News Recommendation](Industry/%5B2016%5D%5BMicrosoft%5D%20User%20Fatigue%20in%20Online%20News%20Recommendation.pdf) - [[2017][Alibaba][DIN] Deep Interest Network for Click-Through Rate Prediction](Industry/%5B2017%5D%5BAlibaba%5D%5BDIN%5D%20Deep%20Interest%20Network%20for%20Click-Through%20Rate%20Prediction.pdf) - [[2017][Alibaba][ATRank] ATRank - An Attention-Based User Behavior Modeling Framework for Recommendation](Industry/%5B2017%5D%5BAlibaba%5D%5BATRank%5D%20ATRank%20-%20An%20Attention-Based%20User%20Behavior%20Modeling%20Framework%20for%20Recommendation.pdf) - [[2018][Alibaba][DIEN] Deep Interest Evolution Network for Click-Through Rate Prediction](Industry/%5B2018%5D%5BAlibaba%5D%5BDIEN%5D%20Deep%20Interest%20Evolution%20Network%20for%20Click-Through%20Rate%20Prediction.pdf) - [[2018][FwFM] Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising](Industry/%5B2018%5D%5BFwFM%5D%20Field-weighted%20Factorization%20Machines%20for%20Click-Through%20Rate%20Prediction%20in%20Display%20Advertising.pdf) - [[2018][JD] Micro Behaviors - A New Perspective in E-commerce Recommender Systems](Industry/%5B2018%5D%5BJD%5D%20Micro%20Behaviors%20-%20A%20New%20Perspective%20in%20E-commerce%20Recommender%20Systems.pdf) - [[2018][Airbnb] Real-time Personalization using Embeddings for Search Ranking at Airbnb](Industry/%5B2018%5D%5BAirbnb%5D%20Real-time%20Personalization%20using%20Embeddings%20for%20Search%20Ranking%20at%20Airbnb.pdf) - [[2019][Alibaba][DSIN] Deep Session Interest Network for Click-Through Rate Prediction](Industry/%5B2019%5D%5BAlibaba%5D%5BDSIN%5D%20Deep%20Session%20Interest%20Network%20for%20Click-Through%20Rate%20Prediction.pdf) - [[2019][Alibaba][BST] Behavior Sequence Transformer for E-commerceRecommendation in Alibaba](Industry/%5B2019%5D%5BAlibaba%5D%5BBST%5D%20Behavior%20Sequence%20Transformer%20for%20E-commerceRecommendation%20in%20Alibaba.pdf) - [[2019][Weibo][FiBiNET] FiBiNET - Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction](Industry/%5B2019%5D%5BWeibo%5D%5BFiBiNET%5D%20FiBiNET%20-%20Combining%20Feature%20Importance%20and%20Bilinear%20feature%20Interaction%20for%20Click-Through%20Rate%20Prediction.pdf) - [[2019][Alibaba][MIMN] Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction](Industry/%5B2019%5D%5BAlibaba%5D%5BMIMN%5D%20Practice%20on%20Long%20Sequential%20User%20Behavior%20Modeling%20for%20Click-Through%20Rate%20Prediction.pdf) - [[2019][Airbnb] Applying Deep Learning To Airbnb Search](Industry/%5B2019%5D%5BAirbnb%5D%20Applying%20Deep%20Learning%20To%20Airbnb%20Search.pdf) - [[2020][Alibaba][CAN] CAN - Revisiting Feature Co-Action for Click-Through Rate Prediction](Industry/%5B2020%5D%5BAlibaba%5D%5BCAN%5D%20CAN%20-%20Revisiting%20Feature%20Co-Action%20for%20Click-Through%20Rate%20Prediction.pdf) - [[2020][Alibaba][SIM] Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction](Industry/%5B2020%5D%5BAlibaba%5D%5BSIM%5D%20Search-based%20User%20Interest%20Modeling%20with%20Lifelong%20Sequential%20Behavior%20Data%20for%20Click-Through%20Rate%20Prediction.pdf) - [[2020][Alibaba][DMR] Deep Match to Rank Model for Personalized Click-Through Rate Prediction](Industry/%5B2020%5D%5BAlibaba%5D%5BDMR%5D%20Deep%20Match%20to%20Rank%20Model%20for%20Personalized%20Click-Through%20Rate%20Prediction.pdf) - [[2021][Fliggy] [DMSN] Spatial-Temporal Deep Intention Destination Networks for Online Travel Planning](Industry/%5B2021%5D%5BFliggy%5D%20%5BDMSN%5D%20Spatial-Temporal%20Deep%20Intention%20Destination%20Networks%20for%20Online%20Travel%20Planning.pdf) - [[2021][Weibo][MaskNet] MaskNet - Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask](Industry/%5B2021%5D%5BWeibo%5D%5BMaskNet%5D%20MaskNet%20-%20Introducing%20Feature-Wise%20Multiplication%20to%20CTR%20Ranking%20Models%20by%20Instance-Guided%20Mask.pdf) - [[2021][Huawei][AutoDis] An Embedding Learning Framework for Numerical Features in CTR Prediction](Industry/%5B2021%5D%5BHuawei%5D%5BAutoDis%5D%20An%20Embedding%20Learning%20Framework%20for%20Numerical%20Features%20in%20CTR%20Prediction.pdf) - [[2021][Alibaba][DINMP] A Non-sequential Approach to Deep User Interest Model for Click-Through Rate Prediction](Industry/%5B2021%5D%5BAlibaba%5D%5BDINMP%5D%20A%20Non-sequential%20Approach%20to%20Deep%20User%20Interest%20Model%20for%20Click-Through%20Rate%20Prediction.pdf) - [[2021][Google] Bootstrapping Recommendations at Chrome Web Store](Industry/%5B2021%5D%5BGoogle%5D%20Bootstrapping%20Recommendations%20at%20Chrome%20Web%20Store.pdf) - [[2022][Alibaba] Towards Understanding the Overfitting Phenomenon of Deep Click-Through Rate Prediction Models](Industry/%5B2022%5D%5BAlibaba%5D%20Towards%20Understanding%20the%20Overfitting%20Phenomenon%20of%20Deep%20Click-Through%20Rate%20Prediction%20Models.pdf) - [[2022][Google] On the Factory Floor - ML Engineering for Industrial-Scale Ads Recommendation Models](Industry/%5B2022%5D%5BGoogle%5D%20On%20the%20Factory%20Floor%20-%20ML%20Engineering%20for%20Industrial-Scale%20Ads%20Recommendation%20Models.pdf) - [[2023][Huawei] Ten Challenges in Industrial Recommender Systems](Industry/%5B2023%5D%5BHuawei%5D%20Ten%20Challenges%20in%20Industrial%20Recommender%20Systems.pdf) - [[2023][Alibaba][JRC] Joint Optimization of Ranking and Calibration with Contextualized Hybrid Model](Industry/%5B2023%5D%5BAlibaba%5D%5BJRC%5D%20Joint%20Optimization%20of%20Ranking%20and%20Calibration%20with%20Contextualized%20Hybrid%20Model.pdf) - [[2023] Methodologies for Improving Modern Industrial Recommender Systems](Industry/%5B2023%5D%20Methodologies%20for%20Improving%20Modern%20Industrial%20Recommender%20Systems.pdf) - [A Comprehensive Summarization and Evaluation of Feature Refinement Modules for CTR Prediction](Industry/A%20Comprehensive%20Summarization%20and%20Evaluation%20of%20Feature%20Refinement%20Modules%20for%20CTR%20Prediction.pdf) - [AutoSeqRec - Autoencoder for Efficient Sequential Recommendation](Industry/AutoSeqRec%20-%20Autoencoder%20for%20Efficient%20Sequential%20Recommendation.pdf) - [Adversarial Mixture Of Experts with Category Hierarchy Soft Constraint](Industry/Adversarial%20Mixture%20Of%20Experts%20with%20Category%20Hierarchy%20Soft%20Constraint.pdf) - [All-domain Moveline Evolution Network for Click-Through Rate Prediction](Industry/All-domain%20Moveline%20Evolution%20Network%20for%20Click-Through%20Rate%20Prediction.pdf) - [Alternating Pointwise-Pairwise Learning for Personalized Item Ranking](Industry/Alternating%20Pointwise-Pairwise%20Learning%20for%20Personalized%20Item%20Ranking.pdf) - [Adversarial Filtering Modeling on Long-term User Behavior Sequences for Click-Through Rate Prediction](Industry/Adversarial%20Filtering%20Modeling%20on%20Long-term%20User%20Behavior%20Sequences%20for%20Click-Through%20Rate%20Prediction.pdf) - [Adaptive Collaborative Filtering with Personalized Time Decay Functions for Financial Product Recommendation](Industry/Adaptive%20Collaborative%20Filtering%20with%20Personalized%20Time%20Decay%20Functions%20for%20Financial%20Product%20Recommendation.pdf) - [A Large Scale Prediction Engine for App Install Clicks and Conversions](Industry/A%20Large%20Scale%20Prediction%20Engine%20for%20App%20Install%20Clicks%20and%20Conversions.pdf) - [AutoMLP - Automated MLP for Sequential Recommendations](Industry/AutoMLP%20-%20Automated%20MLP%20for%20Sequential%20Recommendations.pdf) - [A Deep Behavior Path Matching Network for Click-Through Rate Prediction](Industry/A%20Deep%20Behavior%20Path%20Matching%20Network%20for%20Click-Through%20Rate%20Prediction.pdf) - [Aligned Side Information Fusion Method for Sequential Recommendation](Industry/Aligned%20Side%20Information%20Fusion%20Method%20for%20Sequential%20Recommendation.pdf) - [Attention Mixtures for Time-Aware Sequential Recommendation](Industry/Attention%20Mixtures%20for%20Time-Aware%20Sequential%20Recommendation.pdf) - [A Self-Correcting Sequential Recommender](Industry/A%20Self-Correcting%20Sequential%20Recommender.pdf) - [Beyond Cascaded Architectures - An End-to-end Generative Framework for Industrial Advertising](Industry/Beyond%20Cascaded%20Architectures%20-%20An%20End-to-end%20Generative%20Framework%20for%20Industrial%20Advertising.pdf) - [Breaking the Curse of Quality Saturation with User-Centric Ranking](Industry/Breaking%20the%20Curse%20of%20Quality%20Saturation%20with%20User-Centric%20Ranking.pdf) - [Click-aware Structure Transfer with Sample Weight Assignment for Post-Click Conversion Rate Estimation](Industry/Click-aware%20Structure%20Transfer%20with%20Sample%20Weight%20Assignment%20for%20Post-Click%20Conversion%20Rate%20Estimation.pdf) - [Category-Specific CNN for Visual-aware CTR Prediction at JD.com](Industry/Category-Specific%20CNN%20for%20Visual-aware%20CTR%20Prediction%20at%20JD.com.pdf) - [ConsRec - Learning Consensus Behind Interactions for Group Recommendation](Industry/ConsRec%20-%20Learning%20Consensus%20Behind%20Interactions%20for%20Group%20Recommendation.pdf) - [CSPM - A Contrastive Spatiotemporal Preference Model for CTR Prediction in On-Demand Food Delivery Services](Industry/CSPM%20-%20A%20Contrastive%20Spatiotemporal%20Preference%20Model%20for%20CTR%20Prediction%20in%20On-Demand%20Food%20Delivery%20Services.pdf) - [ContextNet - A Click-Through Rate Prediction Framework Using Contextual information to Refine Feature Embedding](Industry/ContextNet%20-%20A%20Click-Through%20Rate%20Prediction%20Framework%20Using%20Contextual%20information%20to%20Refine%20Feature%20Embedding.pdf) - [CAEN - A Hierarchically Attentive Evolution Network for Item-Attribute-Change-Aware Recommendation in the Growing E-commerce Environment](Industry/CAEN%20-%20A%20Hierarchically%20Attentive%20Evolution%20Network%20for%20Item-Attribute-Change-Aware%20Recommendation%20in%20the%20Growing%20E-commerce%20Environment.pdf) - [Deep Group Interest Network on Full Lifelong User Behaviors for CTR Prediction](Industry/Deep%20Group%20Interest%20Network%20on%20Full%20Lifelong%20User%20Behaviors%20for%20CTR%20Prediction.pdf) - [Decision-Making Context Interaction Network for Click-Through Rate Prediction](Industry/Decision-Making%20Context%20Interaction%20Network%20for%20Click-Through%20Rate%20Prediction.pdf) - [Dual Graph enhanced Embedding Neural Network for CTR Prediction](Industry/Dual%20Graph%20enhanced%20Embedding%20Neural%20Network%20for%20CTR%20Prediction.pdf) - [Deep Interest with Hierarchical Attention Network for Click-Through Rate Prediction](Industry/Deep%20Interest%20with%20Hierarchical%20Attention%20Network%20for%20Click-Through%20Rate%20Prediction.pdf) - [Disentangling Past-Future Modeling in Sequential Recommendation via Dual Networks](Industry/Disentangling%20Past-Future%20Modeling%20in%20Sequential%20Recommendation%20via%20Dual%20Networks.pdf) - [Deep Learning Recommendation Model for Personalization and Recommendation System](Industry/Deep%20Learning%20Recommendation%20Model%20for%20Personalization%20and%20Recommendation%20System.pdf) - [Deep Multiple Quantization Network on Long Behavior Sequence for Click-Through Rate Prediction](Industry/Deep%20Multiple%20Quantization%20Network%20on%20Long%20Behavior%20Sequence%20for%20Click-Through%20Rate%20Prediction.pdf) - [Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction](Industry/Deep%20Spatio-Temporal%20Neural%20Networks%20for%20Click-Through%20Rate%20Prediction.pdf) - [DV365 - Extremely Long User History Modeling at Instagram](Industry/DV365%20-%20Extremely%20Long%20User%20History%20Modeling%20at%20Instagram.pdf) - [Denoising Neural Network for News Recommendation with Positive and Negative Implicit Feedback](Industry/Denoising%20Neural%20Network%20for%20News%20Recommendation%20with%20Positive%20and%20Negative%20Implicit%20Feedback.pdf) - [Denoising User-aware Memory Network for Recommendation](Industry/Denoising%20User-aware%20Memory%20Network%20for%20Recommendation.pdf) - [Deep Context Interest Network for Click-Through Rate Prediction](Industry/Deep%20Context%20Interest%20Network%20for%20Click-Through%20Rate%20Prediction.pdf) - [Disentangling Long and Short-Term Interests for Recommendation](Industry/Disentangling%20Long%20and%20Short-Term%20Interests%20for%20Recommendation.pdf) - [Enhancing CTR Prediction through Sequential Recommendation Pre-training- Introducing the SRP4CTR Framework](Industry/Enhancing%20CTR%20Prediction%20through%20Sequential%20Recommendation%20Pre-training-%20Introducing%20the%20SRP4CTR%20Framework.pdf) - [Ensure Timeliness and Accuracy - A Novel Sliding Window Data Stream Paradigm for Live Streaming Recommendation](Industry/Ensure%20Timeliness%20and%20Accuracy%20-%20A%20Novel%20Sliding%20Window%20Data%20Stream%20Paradigm%20for%20Live%20Streaming%20Recommendation.pdf) - [E-Commerce Item Recommendation Based on Field-aware Factorization Machine](Industry/E-Commerce%20Item%20Recommendation%20Based%20on%20Field-aware%20Factorization%20Machine.pdf) - [Enhancing E-commerce Product Search through Reinforcement Learning-Powered Query Reformulation](Industry/Enhancing%20E-commerce%20Product%20Search%20through%20Reinforcement%20Learning-Powered%20Query%20Reformulation.pdf) - [Enhancing CTR prediction in Recommendation Domain with Search Query Representation](Industry/Enhancing%20CTR%20prediction%20in%20Recommendation%20Domain%20with%20Search%20Query%20Representation.pdf) - [EXTR - Click-Through Rate Prediction with Externalities in E-Commerce Sponsored Search](Industry/EXTR%20-%20Click-Through%20Rate%20Prediction%20with%20Externalities%20in%20E-Commerce%20Sponsored%20Search.pdf) - [End-to-End User Behavior Retrieval in Click-Through Rate Prediction Model](Industry/End-to-End%20User%20Behavior%20Retrieval%20in%20Click-Through%20Rate%20Prediction%20Model.pdf) - [Entire Space Learning Framework- Unbias Conversion Rate Prediction in Full Stages of Recommender System](Industry/Entire%20Space%20Learning%20Framework-%20Unbias%20Conversion%20Rate%20Prediction%20in%20Full%20Stages%20of%20Recommender%20System.pdf) - [EGA-V1 - Unifying Online Advertising with End-to-End Learning](Industry/EGA-V1%20-%20Unifying%20Online%20Advertising%20with%20End-to-End%20Learning.pdf) - [Efficient Long Sequential Low-rank Adaptive Attention for Click-through rate Prediction](Industry/Efficient%20Long%20Sequential%20Low-rank%20Adaptive%20Attention%20for%20Click-through%20rate%20Prediction.pdf) - [EGA-V2 - An End-to-end Generative Framework for Industrial Advertising](Industry/EGA-V2%20-%20An%20End-to-end%20Generative%20Framework%20for%20Industrial%20Advertising.pdf) - [FM2 - Field-matrixed Factorization Machines for Recommender Systems](Industry/FM2%20-%20Field-matrixed%20Factorization%20Machines%20for%20Recommender%20Systems.pdf) - [FeedRec - News Feed Recommendation with Various User Feedbacks](Industry/FeedRec%20-%20News%20Feed%20Recommendation%20with%20Various%20User%20Feedbacks.pdf) - [Fi-GNN - Modeling Feature Interactions via Graph Neural Networks for CTR Prediction](Industry/Fi-GNN%20-%20Modeling%20Feature%20Interactions%20via%20Graph%20Neural%20Networks%20for%20CTR%20Prediction.pdf) - [FLEN - Leveraging Field for Scalable CTR Prediction](Industry/FLEN%20-%20Leveraging%20Field%20for%20Scalable%20CTR%20Prediction.pdf) - [FiBiNet++ - Reducing Model Size by Low Rank Feature Interaction Layer for CTR Prediction](Industry/FiBiNet%2B%2B%20-%20Reducing%20Model%20Size%20by%20Low%20Rank%20Feature%20Interaction%20Layer%20for%20CTR%20Prediction.pdf) - [Fragment and Integrate Network (FIN) - A Novel Spatial-Temporal Modeling Based on Long Sequential Behavior for Online Food Ordering Click-Through Rate Prediction](Industry/Fragment%20and%20Integrate%20Network%20%28FIN%29%20-%20A%20Novel%20Spatial-Temporal%20Modeling%20Based%20on%20Long%20Sequential%20Behavior%20for%20Online%20Food%20Ordering%20Click-Through%20Rate%20Prediction.pdf) - [FiBiNet++ - Improving FiBiNet by Greatly Reducing Model Size for CTR Prediction](Industry/FiBiNet%2B%2B%20-%20Improving%20FiBiNet%20by%20Greatly%20Reducing%20Model%20Size%20for%20CTR%20Prediction.pdf) - [FinalMLP - An Enhanced Two-Stream MLP Model for CTR Prediction](Industry/FinalMLP%20-%20An%20Enhanced%20Two-Stream%20MLP%20Model%20for%20CTR%20Prediction.pdf) - [GateNet - Gating-Enhanced Deep Network for Click-Through Rate Prediction](Industry/GateNet%20-%20Gating-Enhanced%20Deep%20Network%20for%20Click-Through%20Rate%20Prediction.pdf) - [Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems](Industry/Graph-Based%20Model-Agnostic%20Data%20Subsampling%20for%20Recommendation%20Systems.pdf) - [Group-Aware Interest Disentangled Dual-Training for Personalized Recommendation](Industry/Group-Aware%20Interest%20Disentangled%20Dual-Training%20for%20Personalized%20Recommendation.pdf) - [Generative Flow Network for Listwise Recommendation](Industry/Generative%20Flow%20Network%20for%20Listwise%20Recommendation.pdf) - [Hesitation and Tolerance in Recommender Systems](Industry/Hesitation%20and%20Tolerance%20in%20Recommender%20Systems.pdf) - [Hierarchically Fusing Long and Short-Term User Interests for Click-Through Rate Prediction in Product Search](Industry/Hierarchically%20Fusing%20Long%20and%20Short-Term%20User%20Interests%20for%20Click-Through%20Rate%20Prediction%20in%20Product%20Search.pdf) - [Hybrid Interest Modeling for Long-tailed Users](Industry/Hybrid%20Interest%20Modeling%20for%20Long-tailed%20Users.pdf) - [HyFormer - Revisiting the Roles of Sequence Modeling and Feature Interaction in CTR Prediction](Industry/HyFormer%20-%20Revisiting%20the%20Roles%20of%20Sequence%20Modeling%20and%20Feature%20Interaction%20in%20CTR%20Prediction.pdf) - [Hierarchical Gating Networks for Sequential Recommendation](Industry/Hierarchical%20Gating%20Networks%20for%20Sequential%20Recommendation.pdf) - [HIEN - Hierarchical Intention Embedding Network for Click-Through Rate Prediction](Industry/HIEN%20-%20Hierarchical%20Intention%20Embedding%20Network%20for%20Click-Through%20Rate%20Prediction.pdf) - [Inverse Learning with Extremely Sparse Feedback for Recommendation](Industry/Inverse%20Learning%20with%20Extremely%20Sparse%20Feedback%20for%20Recommendation.pdf) - [Improving Sequential Recommender Systems with Online and In-store User Behavior](Industry/Improving%20Sequential%20Recommender%20Systems%20with%20Online%20and%20In-store%20User%20Behavior.pdf) - [Improving Pairwise Learning for Item Recommendation from Implicit Feedback](Industry/Improving%20Pairwise%20Learning%20for%20Item%20Recommendation%20from%20Implicit%20Feedback.pdf) - [Improving Recommendation Quality in Google Drive](Industry/Improving%20Recommendation%20Quality%20in%20Google%20Drive.pdf) - [Incorporating Social-aware User Preference for Video Recommendation](Industry/Incorporating%20Social-aware%20User%20Preference%20for%20Video%20Recommendation.pdf) - [Improving Deep Learning For Airbnb Search](Industry/Improving%20Deep%20Learning%20For%20Airbnb%20Search.pdf) - [Improving Long-tail User CTR Prediction via Hierarchical Distribution Alignment](Industry/Improving%20Long-tail%20User%20CTR%20Prediction%20via%20Hierarchical%20Distribution%20Alignment.pdf) - [Interpretable User Retention Modeling in Recommendation](Industry/Interpretable%20User%20Retention%20Modeling%20in%20Recommendation.pdf) - [Improving feature interactions at Pinterest under industry constraints](Industry/Improving%20feature%20interactions%20at%20Pinterest%20under%20industry%20constraints.pdf) - [Implicit User Awareness Modeling via Candidate Items for CTR Prediction in Search Ads](Industry/Implicit%20User%20Awareness%20Modeling%20via%20Candidate%20Items%20for%20CTR%20Prediction%20in%20Search%20Ads.pdf) - [Incorporating User Micro-behaviors and Item Knowledge into Multi-task Learning for Session-based Recommendation](Industry/Incorporating%20User%20Micro-behaviors%20and%20Item%20Knowledge%20into%20Multi-task%20Learning%20for%20Session-based%20Recommendation.pdf) - [Learning from All Sides - Diversified Positive Augmentation via Self-distillation in Recommendation](Industry/Learning%20from%20All%20Sides%20-%20Diversified%20Positive%20Augmentation%20via%20Self-distillation%20in%20Recommendation.pdf) - [Leveraging Watch-time Feedback for Short-Video Recommendations - A Causal Labeling Framework](Industry/Leveraging%20Watch-time%20Feedback%20for%20Short-Video%20Recommendations%20-%20A%20Causal%20Labeling%20Framework.pdf) - [Long Short-Term Temporal Meta-learning in Online Recommendation](Industry/Long%20Short-Term%20Temporal%20Meta-learning%20in%20Online%20Recommendation.pdf) - [LONG-SEQUENCE RECOMMENDATION MODELS NEED DECOUPLED EMBEDDINGS](Industry/LONG-SEQUENCE%20RECOMMENDATION%20MODELS%20NEED%20DECOUPLED%20EMBEDDINGS.pdf) - [Learning and Optimization of Implicit Negative Feedback for Industrial Short-video Recommender System](Industry/Learning%20and%20Optimization%20of%20Implicit%20Negative%20Feedback%20for%20Industrial%20Short-video%20Recommender%20System.pdf) - [Long-Term Interest Clock - Fine-Grained Time Perception in Streaming Recommendation System](Industry/Long-Term%20Interest%20Clock%20-%20Fine-Grained%20Time%20Perception%20in%20Streaming%20Recommendation%20System.pdf) - [LambdaFM - Learning Optimal Ranking with Factorization Machines Using Lambda Surrogates](Industry/LambdaFM%20-%20Learning%20Optimal%20Ranking%20with%20Factorization%20Machines%20Using%20Lambda%20Surrogates.pdf) - [Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction](Industry/Lifelong%20Sequential%20Modeling%20with%20Personalized%20Memorization%20for%20User%20Response%20Prediction.pdf) - [LONGER - Scaling Up Long Sequence Modeling in Industrial Recommenders](Industry/LONGER%20-%20Scaling%20Up%20Long%20Sequence%20Modeling%20in%20Industrial%20Recommenders.pdf) - [Learning Within-Session Budgets from Browsing Trajectories](Industry/Learning%20Within-Session%20Budgets%20from%20Browsing%20Trajectories.pdf) - [Modeling Users’ Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search](Industry/Modeling%20Users%E2%80%99%20Contextualized%20Page-wise%20Feedback%20for%20Click-Through%20Rate%20Prediction%20in%20E-commerce%20Search.pdf) - [Modeling User Retention through Generative Flow Networks](Industry/Modeling%20User%20Retention%20through%20Generative%20Flow%20Networks.pdf) - [Modeling Multi-aspect Preferences and Intents for Multi-behavioral Sequential Recommendation](Industry/Modeling%20Multi-aspect%20Preferences%20and%20Intents%20for%20Multi-behavioral%20Sequential%20Recommendation.pdf) - [Multi-Granularity Click Confidence Learning via Self-Distillation in Recommendation](Industry/Multi-Granularity%20Click%20Confidence%20Learning%20via%20Self-Distillation%20in%20Recommendation.pdf) - [Making Users Indistinguishable - Attribute-wise Unlearning in Recommender Systems](Industry/Making%20Users%20Indistinguishable%20-%20Attribute-wise%20Unlearning%20in%20Recommender%20Systems.pdf) - [Multi-Epoch Learning for Deep Click-Through Rate Prediction Models](Industry/Multi-Epoch%20Learning%20for%20Deep%20Click-Through%20Rate%20Prediction%20Models.pdf) - [MemoNet - Memorizing All Cross Features’ Representations Efficiently via Multi-Hash Codebook Network for CTR Prediction](Industry/MemoNet%20-%20Memorizing%20All%20Cross%20Features%E2%80%99%20Representations%20Efficiently%20via%20Multi-Hash%20Codebook%20Network%20for%20CTR%20Prediction.pdf) - [Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR Prediction](Industry/Multi-Interactive%20Attention%20Network%20for%20Fine-grained%20Feature%20Learning%20in%20CTR%20Prediction.pdf) - [Modeling Mobile User Actions for Purchase Recommendation using Deep Memory Networks](Industry/Modeling%20Mobile%20User%20Actions%20for%20Purchase%20Recommendation%20using%20Deep%20Memory%20Networks.pdf) - [MRIF - Multi-resolution Interest Fusion for Recommendation](Industry/MRIF%20-%20Multi-resolution%20Interest%20Fusion%20for%20Recommendation.pdf) - [MARM - Unlocking the Future of Recommendation Systems through Memory Augmentation and Scalable Complexity](Industry/MARM%20-%20Unlocking%20the%20Future%20of%20Recommendation%20Systems%20through%20Memory%20Augmentation%20and%20Scalable%20Complexity.pdf) - [Micro-Behavior Encoding for Session-based Recommendation](Industry/Micro-Behavior%20Encoding%20for%20Session-based%20Recommendation.pdf) - [Neural News Recommendation with Negative Feedback](Industry/Neural%20News%20Recommendation%20with%20Negative%20Feedback.pdf) - [News Recommendation with Candidate-aware User Modeling](Industry/News%20Recommendation%20with%20Candidate-aware%20User%20Modeling.pdf) - [Optimizing Feature Set for Click-Through Rate Prediction](Industry/Optimizing%20Feature%20Set%20for%20Click-Through%20Rate%20Prediction.pdf) - [Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data](Industry/Product-based%20Neural%20Networks%20for%20User%20Response%20Prediction%20over%20Multi-field%20Categorical%20Data.pdf) - [PURS - Personalized Unexpected Recommender System for Improving User Satisfaction](Industry/PURS%20-%20Personalized%20Unexpected%20Recommender%20System%20for%20Improving%20User%20Satisfaction.pdf) - [Query-dominant User Interest Network for Large-Scale Search Ranking](Industry/Query-dominant%20User%20Interest%20Network%20for%20Large-Scale%20Search%20Ranking.pdf) - [Recommender Transformers with Behavior Pathways](Industry/Recommender%20Transformers%20with%20Behavior%20Pathways.pdf) - [RUEL - Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation](Industry/RUEL%20-%20Retrieval-Augmented%20User%20Representation%20with%20Edge%20Browser%20Logs%20for%20Sequential%20Recommendation.pdf) - [Res-embedding for Deep Learning Based Click-Through Rate Prediction Modeling](Industry/Res-embedding%20for%20Deep%20Learning%20Based%20Click-Through%20Rate%20Prediction%20Modeling.pdf) - [Reweighting Clicks with Dwell Time in Recommendation](Industry/Reweighting%20Clicks%20with%20Dwell%20Time%20in%20Recommendation.pdf) - [Sequential Modeling with Multiple Attributes for Watchlist Recommendation in E-Commerce](Industry/Sequential%20Modeling%20with%20Multiple%20Attributes%20for%20Watchlist%20Recommendation%20in%20E-Commerce.pdf) - [Sparse Attentive Memory Network for Click-through Rate Prediction with Long Sequences](Industry/Sparse%20Attentive%20Memory%20Network%20for%20Click-through%20Rate%20Prediction%20with%20Long%20Sequences.pdf) - [Surrogate for Long-Term User Experience in Recommender Systems](Industry/Surrogate%20for%20Long-Term%20User%20Experience%20in%20Recommender%20Systems.pdf) - [Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction](Industry/Sampling%20Is%20All%20You%20Need%20on%20Modeling%20Long-Term%20User%20Behaviors%20for%20CTR%20Prediction.pdf) - [TrendSpotter - Forecasting E-commerce Product Trends](Industry/TrendSpotter%20-%20Forecasting%20E-commerce%20Product%20Trends.pdf) - [Triangle Graph Interest Network for Click-through Rate Prediction](Industry/Triangle%20Graph%20Interest%20Network%20for%20Click-through%20Rate%20Prediction.pdf) - [To Copy, or not to Copy; That is a Critical Issue of the Output Softmax Layer in Neural Sequential Recommenders](Industry/To%20Copy%2C%20or%20not%20to%20Copy%3B%20That%20is%20a%20Critical%20Issue%20of%20the%20Output%20Softmax%20Layer%20in%20Neural%20Sequential%20Recommenders.pdf) - [TencentRec - Real-time Stream Recommendation in Practice](Industry/TencentRec%20-%20Real-time%20Stream%20Recommendation%20in%20Practice.pdf) - [Towards Deeper, Lighter and Interpretable Cross Network for CTR Prediction](Industry/Towards%20Deeper%2C%20Lighter%20and%20Interpretable%20Cross%20Network%20for%20CTR%20Prediction.pdf) - [Temporal Interest Network for Click-Through Rate Prediction](Industry/Temporal%20Interest%20Network%20for%20Click-Through%20Rate%20Prediction.pdf) - [TransAct - Transformer-based Realtime User Action Model for Recommendation at Pinterest](Industry/TransAct%20-%20Transformer-based%20Realtime%20User%20Action%20Model%20for%20Recommendation%20at%20Pinterest.pdf) - [TransAct V2 - Lifelong User Action Sequence Modeling on Pinterest Recommendation](Industry/TransAct%20V2%20-%20Lifelong%20User%20Action%20Sequence%20Modeling%20on%20Pinterest%20Recommendation.pdf) - [TWIN - TWo-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction at Kuaishou](Industry/TWIN%20-%20TWo-stage%20Interest%20Network%20for%20Lifelong%20User%20Behavior%20Modeling%20in%20CTR%20Prediction%20at%20Kuaishou.pdf) - [TWIN V2 - Scaling Ultra-Long User Behavior Sequence Modeling for Enhanced CTR Prediction at Kuaishou](Industry/TWIN%20V2%20-%20Scaling%20Ultra-Long%20User%20Behavior%20Sequence%20Modeling%20for%20Enhanced%20CTR%20Prediction%20at%20Kuaishou.pdf) - [TiSSA - A Time Slice Self-Attention Approach for Modeling Sequential User Behaviors](Industry/TiSSA%20-%20A%20Time%20Slice%20Self-Attention%20Approach%20for%20Modeling%20Sequential%20User%20Behaviors.pdf) - [User Behavior Retrieval for Click-Through Rate Prediction](Industry/User%20Behavior%20Retrieval%20for%20Click-Through%20Rate%20Prediction.pdf) - [Visualizing and Understanding Deep Neural Networks in CTR Prediction](Industry/Visualizing%20and%20Understanding%20Deep%20Neural%20Networks%20in%20CTR%20Prediction.pdf) - [Variance Reduction Using In-Experiment Data- Efficient and Targeted Online Measurement for Sparse and Delayed Outcomes](Industry/Variance%20Reduction%20Using%20In-Experiment%20Data-%20Efficient%20and%20Targeted%20Online%20Measurement%20for%20Sparse%20and%20Delayed%20Outcomes.pdf) #### TriggerInduced - [[2021][Tencent][R3S] Real-time Relevant Recommendation Suggestion](Industry/TriggerInduced/%5B2021%5D%5BTencent%5D%5BR3S%5D%20Real-time%20Relevant%20Recommendation%20Suggestion.pdf) - [[2022][Alibaba][DIAN] Deep Intention-Aware Network for Click-Through Rate Prediction](Industry/TriggerInduced/%5B2022%5D%5BAlibaba%5D%5BDIAN%5D%20Deep%20Intention-Aware%20Network%20for%20Click-Through%20Rate%20Prediction.pdf) - [[2022][Alibaba][DIHN] Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced Recommendation](Industry/TriggerInduced/%5B2022%5D%5BAlibaba%5D%5BDIHN%5D%20Deep%20Interest%20Highlight%20Network%20for%20Click-Through%20Rate%20Prediction%20in%20Trigger-Induced%20Recommendation.pdf) - [Click A, Buy B - Rethinking Conversion Attribution in ECommerce Recommendations](Industry/TriggerInduced/Click%20A%2C%20Buy%20B%20-%20Rethinking%20Conversion%20Attribution%20in%20ECommerce%20Recommendations.pdf) - [Collaborative Contrastive Network for Click-Through Rate Prediction](Industry/TriggerInduced/Collaborative%20Contrastive%20Network%20for%20Click-Through%20Rate%20Prediction.pdf) - [DPAN - Dynamic Preference-based and Attribute-aware Network for Relevant Recommendations](Industry/TriggerInduced/DPAN%20-%20Dynamic%20Preference-based%20and%20Attribute-aware%20Network%20for%20Relevant%20Recommendations.pdf) - [Deep Evolutional Instant Interest Network for CTR Prediction in Trigger-Induced Recommendation](Industry/TriggerInduced/Deep%20Evolutional%20Instant%20Interest%20Network%20for%20CTR%20Prediction%20in%20Trigger-Induced%20Recommendation.pdf) - [Modeling User Intent Beyond Trigger - Incorporating Uncertainty for Trigger-Induced Recommendation](Industry/TriggerInduced/Modeling%20User%20Intent%20Beyond%20Trigger%20-%20Incorporating%20Uncertainty%20for%20Trigger-Induced%20Recommendation.pdf) #### Reciprocal - [[2010] RECON - A Reciprocal Recommender for Online Dating](Industry/Reciprocal/%5B2010%5D%20RECON%20-%20A%20Reciprocal%20Recommender%20for%20Online%20Dating.pdf) - [[2019] Latent Factor Models and Aggregation Operators for Collaborative Filtering in Reciprocal Recommender Systems](Industry/Reciprocal/%5B2019%5D%20Latent%20Factor%20Models%20and%20Aggregation%20Operators%20for%20Collaborative%20Filtering%20in%20Reciprocal%20Recommender%20Systems.pdf) - [[2022] MATCHING THEORY-BASED RECOMMENDER SYSTEMS IN ONLINE DATING](Industry/Reciprocal/%5B2022%5D%20MATCHING%20THEORY-BASED%20RECOMMENDER%20SYSTEMS%20IN%20ONLINE%20DATING.pdf) - [[2022][Boss][DPGNN] Modeling Two-Way Selection Preference for Person-Job Fit](Industry/Reciprocal/%5B2022%5D%5BBoss%5D%5BDPGNN%5D%20Modeling%20Two-Way%20Selection%20Preference%20for%20Person-Job%20Fit.pdf) - [BOSS - A Bilateral Occupational-Suitability-Aware Recommender System for Online Recruitment](Industry/Reciprocal/BOSS%20-%20A%20Bilateral%20Occupational-Suitability-Aware%20Recommender%20System%20for%20Online%20Recruitment.pdf) - [CUPID - A Real-Time Session-Based Reciprocal Recommendation System for a One-on-One Social Discovery Platform](Industry/Reciprocal/CUPID%20-%20A%20Real-Time%20Session-Based%20Reciprocal%20Recommendation%20System%20for%20a%20One-on-One%20Social%20Discovery%20Platform.pdf) - [Optimally Balancing Receiver and Recommended Users’ Importance in Reciprocal Recommender Systems](Industry/Reciprocal/Optimally%20Balancing%20Receiver%20and%20Recommended%20Users%E2%80%99%20Importance%20in%20Reciprocal%20Recommender%20Systems.pdf) - [Providing Explanations for Recommendations in Reciprocal Environments](Industry/Reciprocal/Providing%20Explanations%20for%20Recommendations%20in%20Reciprocal%20Environments.pdf) - [Reciprocal Recommendation for Job Matching with Bidirectional Feedback](Industry/Reciprocal/Reciprocal%20Recommendation%20for%20Job%20Matching%20with%20Bidirectional%20Feedback.pdf) - [Reciprocal Recommendation System for Online Dating](Industry/Reciprocal/Reciprocal%20Recommendation%20System%20for%20Online%20Dating.pdf) - [Reciprocal Sequential Recommendation](Industry/Reciprocal/Reciprocal%20Sequential%20Recommendation.pdf) - [Reciprocal Recommendation Algorithm for the Field of Recruitment](Industry/Reciprocal/Reciprocal%20Recommendation%20Algorithm%20for%20the%20Field%20of%20Recruitment.pdf) - [Supporting users in finding successful matches in reciprocal recommender systems](Industry/Reciprocal/Supporting%20users%20in%20finding%20successful%20matches%20in%20reciprocal%20recommender%20systems.pdf) #### Dataset - [A Content-Driven Micro-Video Recommendation Dataset at Scale](Industry/Dataset/A%20Content-Driven%20Micro-Video%20Recommendation%20Dataset%20at%20Scale.pdf) - [KuaiRand - An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos](Industry/Dataset/KuaiRand%20-%20An%20Unbiased%20Sequential%20Recommendation%20Dataset%20with%20Randomly%20Exposed%20Videos.pdf) - [KuaiSAR - A Unified Search And Recommendation Dataset](Industry/Dataset/KuaiSAR%20-%20A%20Unified%20Search%20And%20Recommendation%20Dataset.pdf) - [KuaiRec - A Fully-observed Dataset and Insights for Evaluating Recommender Systems](Industry/Dataset/KuaiRec%20-%20A%20Fully-observed%20Dataset%20and%20Insights%20for%20Evaluating%20Recommender%20Systems.pdf) - [MobileRec - A Large-Scale Dataset for Mobile Apps Recommendation](Industry/Dataset/MobileRec%20-%20A%20Large-Scale%20Dataset%20for%20Mobile%20Apps%20Recommendation.pdf) - [RECFLOW - AN INDUSTRIAL FULL FLOW RECOMMENDATION DATASET](Industry/Dataset/RECFLOW%20-%20AN%20INDUSTRIAL%20FULL%20FLOW%20RECOMMENDATION%20DATASET.pdf) - [REASONER - An Explainable Recommendation Dataset with Multi-aspect Real User Labeled Ground Truths](Industry/Dataset/REASONER%20-%20An%20Explainable%20Recommendation%20Dataset%20with%20Multi-aspect%20Real%20User%20Labeled%20Ground%20Truths.pdf) - [Tenrec - A Large-scale Multipurpose Benchmark Dataset for Recommender Systems](Industry/Dataset/Tenrec%20-%20A%20Large-scale%20Multipurpose%20Benchmark%20Dataset%20for%20Recommender%20Systems.pdf) - [U-NEED - A Fine-grained Dataset for User Needs-Centric E-commerce Conversational Recommendation](Industry/Dataset/U-NEED%20-%20A%20Fine-grained%20Dataset%20for%20User%20Needs-Centric%20E-commerce%20Conversational%20Recommendation.pdf) #### CreativeSelection - [[2019][Bytedance] What You Look Matters? Offline Evaluation of Advertising Creatives for Cold-start Problem](Industry/CreativeSelection/%5B2019%5D%5BBytedance%5D%20What%20You%20Look%20Matters%3F%20Offline%20Evaluation%20of%20Advertising%20Creatives%20for%20Cold-start%20Problem.pdf) - [[2021][Baidu][GemNN] GemNN - Gating-Enhanced Multi-Task Neural Networks with Feature Interaction Learning for CTR Prediction](Industry/CreativeSelection/%5B2021%5D%5BBaidu%5D%5BGemNN%5D%20GemNN%20-%20Gating-Enhanced%20Multi-Task%20Neural%20Networks%20with%20Feature%20Interaction%20Learning%20for%20CTR%20Prediction.pdf) - [Automated Creative Optimization for E-Commerce Advertising](Industry/CreativeSelection/Automated%20Creative%20Optimization%20for%20E-Commerce%20Advertising.pdf) - [A Hybrid Bandit Model with Visual Priors for Creative Ranking in Display Advertising](Industry/CreativeSelection/A%20Hybrid%20Bandit%20Model%20with%20Visual%20Priors%20for%20Creative%20Ranking%20in%20Display%20Advertising.pdf) - [CREATER - CTR-driven Advertising Text Generation with Controlled Pre-Training and Contrastive Fine-Tuning](Industry/CreativeSelection/CREATER%20-%20CTR-driven%20Advertising%20Text%20Generation%20with%20Controlled%20Pre-Training%20and%20Contrastive%20Fine-Tuning.pdf) - [Efficient Optimal Selection for Composited Advertising Creatives with Tree Structure](Industry/CreativeSelection/Efficient%20Optimal%20Selection%20for%20Composited%20Advertising%20Creatives%20with%20Tree%20Structure.pdf) - [Learning to Create Better Ads- Generation and Ranking Approaches for Ad Creative Refinement](Industry/CreativeSelection/Learning%20to%20Create%20Better%20Ads-%20Generation%20and%20Ranking%20Approaches%20for%20Ad%20Creative%20Refinement.pdf) #### NegativeFeedback - [[2020][Tencent][DFN] Deep Feedback Network for Recommendation](Industry/NegativeFeedback/%5B2020%5D%5BTencent%5D%5BDFN%5D%20Deep%20Feedback%20Network%20for%20Recommendation.pdf) - [[2021][Tencent][CDR] Curriculum Disentangled Recommendation with Noisy Multi-feedback](Industry/NegativeFeedback/%5B2021%5D%5BTencent%5D%5BCDR%5D%20Curriculum%20Disentangled%20Recommendation%20with%20Noisy%20Multi-feedback.pdf) - [Learning from Negative User Feedback and Measuring Responsiveness for Sequential Recommenders](Industry/NegativeFeedback/Learning%20from%20Negative%20User%20Feedback%20and%20Measuring%20Responsiveness%20for%20Sequential%20Recommenders.pdf) #### BigPromotion - [Capturing Conversion Rate Fluctuation during Sales Promotions - A Novel Historical Data Reuse Approach](Industry/BigPromotion/Capturing%20Conversion%20Rate%20Fluctuation%20during%20Sales%20Promotions%20-%20A%20Novel%20Historical%20Data%20Reuse%20Approach.pdf) - [Multi-task based Sales Predictions for Online Promotions](Industry/BigPromotion/Multi-task%20based%20Sales%20Predictions%20for%20Online%20Promotions.pdf) - [TemporalExpertNet - Cross-Temporal Knowledge Reuse forPromotion-Aware CVR Prediction](Industry/BigPromotion/TemporalExpertNet%20-%20Cross-Temporal%20Knowledge%20Reuse%20forPromotion-Aware%20CVR%20Prediction.pdf) #### LifetimeValue - [ADSNet - Cross-Domain LTV Prediction with an Adaptive Siamese Network in Advertising](Industry/LifetimeValue/ADSNet%20-%20Cross-Domain%20LTV%20Prediction%20with%20an%20Adaptive%20Siamese%20Network%20in%20Advertising.pdf) - [Out of the Box Thinking - Improving Customer Lifetime Value Modelling via Expert Routing and Game Whale Detection](Industry/LifetimeValue/Out%20of%20the%20Box%20Thinking%20-%20Improving%20Customer%20Lifetime%20Value%20Modelling%20via%20Expert%20Routing%20and%20Game%20Whale%20Detection.pdf) - [OptDist - Learning Optimal Distribution for Customer Lifetime Value Prediction](Industry/LifetimeValue/OptDist%20-%20Learning%20Optimal%20Distribution%20for%20Customer%20Lifetime%20Value%20Prediction.pdf) #### Bundle - [Bundle Recommendation with Graph Convolutional Networks](Industry/Bundle/Bundle%20Recommendation%20with%20Graph%20Convolutional%20Networks.pdf) - [Bundle MCR - Towards Conversational Bundle Recommendation](Industry/Bundle/Bundle%20MCR%20-%20Towards%20Conversational%20Bundle%20Recommendation.pdf) - [CrossCBR - Cross-view Contrastive Learning for Bundle Recommendation](Industry/Bundle/CrossCBR%20-%20Cross-view%20Contrastive%20Learning%20for%20Bundle%20Recommendation.pdf) - [Hierarchical Fashion Graph Network for Personalized Outfit Recommendation](Industry/Bundle/Hierarchical%20Fashion%20Graph%20Network%20for%20Personalized%20Outfit%20Recommendation.pdf) - [POG - Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashion](Industry/Bundle/POG%20-%20Personalized%20Outfit%20Generation%20for%20Fashion%20Recommendation%20at%20Alibaba%20iFashion.pdf) #### Edge - [Real-time Short Video Recommendation on Mobile Devices](Industry/Edge/Real-time%20Short%20Video%20Recommendation%20on%20Mobile%20Devices.pdf) #### FeatureSelection - [AutoField - Automating Feature Selection in Deep Recommender Systems](Industry/FeatureSelection/AutoField%20-%20Automating%20Feature%20Selection%20in%20Deep%20Recommender%20Systems.pdf) - [MvFS - Multi-view Feature Selection for Recommender System](Industry/FeatureSelection/MvFS%20-%20Multi-view%20Feature%20Selection%20for%20Recommender%20System.pdf) - [SHARK - A Lightweight Model Compression Approach for Large-scale Recommender Systems](Industry/FeatureSelection/SHARK%20-%20A%20Lightweight%20Model%20Compression%20Approach%20for%20Large-scale%20Recommender%20Systems.pdf) #### RepeatConsumption - [[2022][Tencent][NoveNet] Modeling User Repeat Consumption Behavior for Online Novel Recommendation](Industry/RepeatConsumption/%5B2022%5D%5BTencent%5D%5BNoveNet%5D%20Modeling%20User%20Repeat%20Consumption%20Behavior%20for%20Online%20Novel%20Recommendation.pdf) - [Buy It Again - Modeling Repeat Purchase Recommendations](Industry/RepeatConsumption/Buy%20It%20Again%20-%20Modeling%20Repeat%20Purchase%20Recommendations.pdf) - [FIM - Frequency-Aware Multi-View Interest Modeling for Local-Life Service Recommendation](Industry/RepeatConsumption/FIM%20-%20Frequency-Aware%20Multi-View%20Interest%20Modeling%20for%20Local-Life%20Service%20Recommendation.pdf) - [Modeling Item-Specific Temporal Dynamics of Repeat Consumption for Recommender Systems](Industry/RepeatConsumption/Modeling%20Item-Specific%20Temporal%20Dynamics%20of%20Repeat%20Consumption%20for%20Recommender%20Systems.pdf) - [On the Value of Reminders within E-Commerce Recommendations](Industry/RepeatConsumption/On%20the%20Value%20of%20Reminders%20within%20E-Commerce%20Recommendations.pdf) - [Predicting Consumption Patterns with Repeated and Novel Events](Industry/RepeatConsumption/Predicting%20Consumption%20Patterns%20with%20Repeated%20and%20Novel%20Events.pdf) - [Predicting Music Relistening Behavior Using the ACT-R Framework](Industry/RepeatConsumption/Predicting%20Music%20Relistening%20Behavior%20Using%20the%20ACT-R%20Framework.pdf) - [Personalized Category Frequency prediction for Buy It Again recommendations](Industry/RepeatConsumption/Personalized%20Category%20Frequency%20prediction%20for%20Buy%20It%20Again%20recommendations.pdf) - [Recommendation on Live-Streaming Platforms - Dynamic Availability and Repeat Consumption](Industry/RepeatConsumption/Recommendation%20on%20Live-Streaming%20Platforms%20-%20Dynamic%20Availability%20and%20Repeat%20Consumption.pdf) - [RepeatNet - A Repeat Aware Neural Recommendation Machine for Session-based Recommendation](Industry/RepeatConsumption/RepeatNet%20-%20A%20Repeat%20Aware%20Neural%20Recommendation%20Machine%20for%20Session-based%20Recommendation.pdf) - [Recommendation for Repeat Consumption from User Implicit Feedback](Industry/RepeatConsumption/Recommendation%20for%20Repeat%20Consumption%20from%20User%20Implicit%20Feedback.pdf) - [The Dynamics of Repeat Consumption](Industry/RepeatConsumption/The%20Dynamics%20of%20Repeat%20Consumption.pdf) - [Will You “Reconsume” the Near Past? Fast Prediction on Short-Term Reconsumption Behaviors](Industry/RepeatConsumption/Will%20You%20%E2%80%9CReconsume%E2%80%9D%20the%20Near%20Past%3F%20Fast%20Prediction%20on%20Short-Term%20Reconsumption%20Behaviors.pdf) #### POI - [[2020][meituan][STGCN] STGCN - A Spatial-Temporal Aware Graph Learning Method for POI Recommendation](Industry/POI/%5B2020%5D%5Bmeituan%5D%5BSTGCN%5D%20STGCN%20-%20A%20Spatial-Temporal%20Aware%20Graph%20Learning%20Method%20for%20POI%20Recommendation.pdf) - [A Survey on Deep Learning Based Point-Of-Interest (POI) Recommendations](Industry/POI/A%20Survey%20on%20Deep%20Learning%20Based%20Point-Of-Interest%20%28POI%29%20Recommendations.pdf) - [A Survey on Point-of-Interest Recommendations Leveraging Heterogeneous Data](Industry/POI/A%20Survey%20on%20Point-of-Interest%20Recommendations%20Leveraging%20Heterogeneous%20Data.pdf) - [A Multi-Channel Next POI Recommendation Framework with Multi-Granularity Check-in Signals](Industry/POI/A%20Multi-Channel%20Next%20POI%20Recommendation%20Framework%20with%20Multi-Granularity%20Check-in%20Signals.pdf) - [Empowering Next POI Recommendation with Multi-Relational Modeling](Industry/POI/Empowering%20Next%20POI%20Recommendation%20with%20Multi-Relational%20Modeling.pdf) - [Exploring the Impact of Temporal Bias in Point-of-Interest Recommendation](Industry/POI/Exploring%20the%20Impact%20of%20Temporal%20Bias%20in%20Point-of-Interest%20Recommendation.pdf) - [GETNext - Trajectory Flow Map Enhanced Transformer for Next POI Recommendation](Industry/POI/GETNext%20-%20Trajectory%20Flow%20Map%20Enhanced%20Transformer%20for%20Next%20POI%20Recommendation.pdf) - [Hierarchical Multi-Task Graph Recurrent Network for Next POI Recommendation](Industry/POI/Hierarchical%20Multi-Task%20Graph%20Recurrent%20Network%20for%20Next%20POI%20Recommendation.pdf) - [Location Embeddings for Next Trip Recommendation](Industry/POI/Location%20Embeddings%20for%20Next%20Trip%20Recommendation.pdf) - [LightMove - A Lightweight Next-POI Recommendation for Taxicab Rooftop Advertising](Industry/POI/LightMove%20-%20A%20Lightweight%20Next-POI%20Recommendation%20for%20Taxicab%20Rooftop%20Advertising.pdf) - [Modeling Spatio-temporal Neighbourhood for Personalized Point-of-interest Recommendation](Industry/POI/Modeling%20Spatio-temporal%20Neighbourhood%20for%20Personalized%20Point-of-interest%20Recommendation.pdf) - [Next Point-of-Interest Recommendation with Inferring Multi-step Future Preferences](Industry/POI/Next%20Point-of-Interest%20Recommendation%20with%20Inferring%20Multi-step%20Future%20Preferences.pdf) - [Online POI Recommendation - Learning Dynamic Geo-Human Interactions in Streams](Industry/POI/Online%20POI%20Recommendation%20-%20Learning%20Dynamic%20Geo-Human%20Interactions%20in%20Streams.pdf) - [ODNET - A Novel Personalized Origin-Destination Ranking Network for Flight Recommendation](Industry/POI/ODNET%20-%20A%20Novel%20Personalized%20Origin-Destination%20Ranking%20Network%20for%20Flight%20Recommendation.pdf) - [Point-of-Interest Recommender Systems based on Location-Based Social Networks - A Survey from an Experimental Perspective](Industry/POI/Point-of-Interest%20Recommender%20Systems%20based%20on%20Location-Based%20Social%20Networks%20-%20A%20Survey%20from%20an%20Experimental%20Perspective.pdf) - [POINTREC - A Test Collection for Narrative-driven Point of Interest Recommendation](Industry/POI/POINTREC%20-%20A%20Test%20Collection%20for%20Narrative-driven%20Point%20of%20Interest%20Recommendation.pdf) - [Personalized Long- and Short-term Preference Learning for Next POI Recommendation](Industry/POI/Personalized%20Long-%20and%20Short-term%20Preference%20Learning%20for%20Next%20POI%20Recommendation.pdf) - [STGIN - Spatial-Temporal Graph Interaction Network for Large-scale POI Recommendation](Industry/POI/STGIN%20-%20Spatial-Temporal%20Graph%20Interaction%20Network%20for%20Large-scale%20POI%20Recommendation.pdf) - [ST-PIL - Spatial-Temporal Periodic Interest Learning for Next Point-of-Interest Recommendation](Industry/POI/ST-PIL%20-%20Spatial-Temporal%20Periodic%20Interest%20Learning%20for%20Next%20Point-of-Interest%20Recommendation.pdf) - [TADSAM - A Time-Aware Dynamic Self-Attention Model for Next Point-of-Interest Recommendation](Industry/POI/TADSAM%20-%20A%20Time-Aware%20Dynamic%20Self-Attention%20Model%20for%20Next%20Point-of-Interest%20Recommendation.pdf) - [When Online Meets Offline - Exploring Periodicity for Travel Destination Prediction](Industry/POI/When%20Online%20Meets%20Offline%20-%20Exploring%20Periodicity%20for%20Travel%20Destination%20Prediction.pdf) - [Where to Go Next - A Spatio-Temporal Gated Network for Next POI Recommendation](Industry/POI/Where%20to%20Go%20Next%20-%20A%20Spatio-Temporal%20Gated%20Network%20for%20Next%20POI%20Recommendation.pdf) - [Why We Go Where We Go - Profiling User Decisions on Choosing POIs](Industry/POI/Why%20We%20Go%20Where%20We%20Go%20-%20Profiling%20User%20Decisions%20on%20Choosing%20POIs.pdf) - [Where to Go Next - Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation](Industry/POI/Where%20to%20Go%20Next%20-%20Modeling%20Long-%20and%20Short-Term%20User%20Preferences%20for%20Point-of-Interest%20Recommendation.pdf) - [You Are What and Where You Are - Graph Enhanced Attention Network for Explainable POI Recommendation](Industry/POI/You%20Are%20What%20and%20Where%20You%20Are%20-%20Graph%20Enhanced%20Attention%20Network%20for%20Explainable%20POI%20Recommendation.pdf) #### Intent - [Automatically Discovering User Consumption Intents in Meituan](Industry/Intent/Automatically%20Discovering%20User%20Consumption%20Intents%20in%20Meituan.pdf) - [FINN - Feedback Interactive Neural Network for Intent Recommendation](Industry/Intent/FINN%20-%20Feedback%20Interactive%20Neural%20Network%20for%20Intent%20Recommendation.pdf) - [Learning to Personalize Recommendations based on Customers’ Shopping Intents](Industry/Intent/Learning%20to%20Personalize%20Recommendations%20based%20on%20Customers%E2%80%99%20Shopping%20Intents.pdf) - [Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation](Industry/Intent/Metapath-guided%20Heterogeneous%20Graph%20Neural%20Network%20for%20Intent%20Recommendation.pdf) - [NEON - Living Needs Prediction System in Meituan](Industry/Intent/NEON%20-%20Living%20Needs%20Prediction%20System%20in%20Meituan.pdf) - [USD - A User-Intent-Driven Sampling and Dual-Debiasing Framework for Large-Scale Homepage Recommendations](Industry/Intent/USD%20-%20A%20User-Intent-Driven%20Sampling%20and%20Dual-Debiasing%20Framework%20for%20Large-Scale%20Homepage%20Recommendations.pdf) #### Representation - [[2020][Tencent][AETN] General-Purpose User Embeddings based on Mobile App Usage](Industry/Representation/%5B2020%5D%5BTencent%5D%5BAETN%5D%20General-Purpose%20User%20Embeddings%20based%20on%20Mobile%20App%20Usage.pdf) - [[2022][Pinterest][PinnerFormer] PinnerFormer - Sequence Modeling for User Representation at Pinterest](Industry/Representation/%5B2022%5D%5BPinterest%5D%5BPinnerFormer%5D%20PinnerFormer%20-%20Sequence%20Modeling%20for%20User%20Representation%20at%20Pinterest.pdf) - [Empowering General-purpose User Representation with Full-life Cycle Behavior Modeling](Industry/Representation/Empowering%20General-purpose%20User%20Representation%20with%20Full-life%20Cycle%20Behavior%20Modeling.pdf) #### FeatureHashing - [[2020][Twitter] Model Size Reduction Using Frequency Based Double Hashing for Recommender Systems](Industry/FeatureHashing/%5B2020%5D%5BTwitter%5D%20Model%20Size%20Reduction%20Using%20Frequency%20Based%20Double%20Hashing%20for%20Recommender%20Systems.pdf) - [[2021][Google][DHE] Learning to Embed Categorical Features without Embedding Tables for Recommendation](Industry/FeatureHashing/%5B2021%5D%5BGoogle%5D%5BDHE%5D%20Learning%20to%20Embed%20Categorical%20Features%20without%20Embedding%20Tables%20for%20Recommendation.pdf) - [AdaS&S - a One-Shot Supernet Approach for Automatic Embedding Size Search in Deep Recommender System](Industry/FeatureHashing/AdaS%26S%20-%20a%20One-Shot%20Supernet%20Approach%20for%20Automatic%20Embedding%20Size%20Search%20in%20Deep%20Recommender%20System.pdf) - [AutoEmb - Automated Embedding Dimensionality Search in Streaming Recommendations](Industry/FeatureHashing/AutoEmb%20-%20Automated%20Embedding%20Dimensionality%20Search%20in%20Streaming%20Recommendations.pdf) - [Adaptive Low-Precision Training for Embeddings in Click-Through Rate Prediction](Industry/FeatureHashing/Adaptive%20Low-Precision%20Training%20for%20Embeddings%20in%20Click-Through%20Rate%20Prediction.pdf) - [Binary Code based Hash Embedding for Web-scale Applications](Industry/FeatureHashing/Binary%20Code%20based%20Hash%20Embedding%20for%20Web-scale%20Applications.pdf) - [Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems](Industry/FeatureHashing/Compositional%20Embeddings%20Using%20Complementary%20Partitions%20for%20Memory-Efficient%20Recommendation%20Systems.pdf) - [Dynamic Embedding Size Search with Minimum Regret for Streaming Recommender System](Industry/FeatureHashing/Dynamic%20Embedding%20Size%20Search%20with%20Minimum%20Regret%20for%20Streaming%20Recommender%20System.pdf) - [Feature Hashing for Large Scale Multitask Learning](Industry/FeatureHashing/Feature%20Hashing%20for%20Large%20Scale%20Multitask%20Learning.pdf) - [Getting Deep Recommenders Fit - Bloom Embeddings for Sparse Binary Input Output Networks](Industry/FeatureHashing/Getting%20Deep%20Recommenders%20Fit%20-%20Bloom%20Embeddings%20for%20Sparse%20Binary%20Input%20Output%20Networks.pdf) - [Hash Embeddings for Efficient Word Representations](Industry/FeatureHashing/Hash%20Embeddings%20for%20Efficient%20Word%20Representations.pdf) - [Learning Effective and Efficient Embedding via an Adaptively-Masked Twins-based Layer](Industry/FeatureHashing/Learning%20Effective%20and%20Efficient%20Embedding%20via%20an%20Adaptively-Masked%20Twins-based%20Layer.pdf) - [Memory-efficient Embedding for Recommendations](Industry/FeatureHashing/Memory-efficient%20Embedding%20for%20Recommendations.pdf) #### Interactive - [Q&R - A Two-Stage Approach toward Interactive Recommendation](Industry/Interactive/Q%26R%20-%20A%20Two-Stage%20Approach%20toward%20Interactive%20Recommendation.pdf) #### IncrementalLearning - [ADER - Adaptively Distilled Exemplar Replay Towards Continual Learning for Session-based Recommendation](Industry/IncrementalLearning/ADER%20-%20Adaptively%20Distilled%20Exemplar%20Replay%20Towards%20Continual%20Learning%20for%20Session-based%20Recommendation.pdf) - [A Survey on Incremental Update for Neural Recommender Systems](Industry/IncrementalLearning/A%20Survey%20on%20Incremental%20Update%20for%20Neural%20Recommender%20Systems.pdf) #### Regression - [[2014][Yahoo] Beyond Clicks - Dwell Time for Personalization](Industry/Regression/%5B2014%5D%5BYahoo%5D%20Beyond%20Clicks%20-%20Dwell%20Time%20for%20Personalization.pdf) - [Conditional Quantile Estimation for Uncertain Watch Time in Short-Video Recommendation](Industry/Regression/Conditional%20Quantile%20Estimation%20for%20Uncertain%20Watch%20Time%20in%20Short-Video%20Recommendation.pdf) - [CREAD - A Classification-Restoration Framework with Error Adaptive Discretization for Watch Time Prediction in Video Recommender Systems](Industry/Regression/CREAD%20-%20A%20Classification-Restoration%20Framework%20with%20Error%20Adaptive%20Discretization%20for%20Watch%20Time%20Prediction%20in%20Video%20Recommender%20Systems.pdf) - [Deconfounding Duration Bias in Watch-time Prediction for Video Recommendation](Industry/Regression/Deconfounding%20Duration%20Bias%20in%20Watch-time%20Prediction%20for%20Video%20Recommendation.pdf) - [Explicit Uncertainty Modeling for Video Watch Time Prediction](Industry/Regression/Explicit%20Uncertainty%20Modeling%20for%20Video%20Watch%20Time%20Prediction.pdf) - [TranSUN - A Preemptive Paradigm to Eradicate Retransformation Bias Intrinsically from Regression Models in Recommender Systems](Industry/Regression/TranSUN%20-%20A%20Preemptive%20Paradigm%20to%20Eradicate%20Retransformation%20Bias%20Intrinsically%20from%20Regression%20Models%20in%20Recommender%20Systems.pdf) - [Tree based Progressive Regression Model for Watch-Time Prediction in Short-video Recommendation](Industry/Regression/Tree%20based%20Progressive%20Regression%20Model%20for%20Watch-Time%20Prediction%20in%20Short-video%20Recommendation.pdf) #### AutomaticPlaylistContinuation - [A hybrid two-stage recommender system for automatic playlist continuation](Industry/AutomaticPlaylistContinuation/A%20hybrid%20two-stage%20recommender%20system%20for%20automatic%20playlist%20continuation.pdf) - [A Line in the Sandv- Recommendation or Ad-hoc Retrieval ](Industry/AutomaticPlaylistContinuation/A%20Line%20in%20the%20Sandv-%20Recommendation%20or%20Ad-hoc%20Retrieval%20.pdf) - [Automatic Playlist Continuation using Subprofile-Aware Diversification](Industry/AutomaticPlaylistContinuation/Automatic%20Playlist%20Continuation%20using%20Subprofile-Aware%20Diversification.pdf) - [A Hybrid Recommender System for Improving Automatic Playlist Continuation](Industry/AutomaticPlaylistContinuation/A%20Hybrid%20Recommender%20System%20for%20Improving%20Automatic%20Playlist%20Continuation.pdf) - [Adversarial Mahalanobis Distance-based Attentive Song Recommender for Automatic Playlist Continuation](Industry/AutomaticPlaylistContinuation/Adversarial%20Mahalanobis%20Distance-based%20Attentive%20Song%20Recommender%20for%20Automatic%20Playlist%20Continuation.pdf) - [Automatic playlist continuation using a hybrid recommender system combining features from text and audio](Industry/AutomaticPlaylistContinuation/Automatic%20playlist%20continuation%20using%20a%20hybrid%20recommender%20system%20combining%20features%20from%20text%20and%20audio.pdf) - [An Ensemble Approach of Recurrent Neural Networks using Pre-Trained Embeddings for Playlist Completion](Industry/AutomaticPlaylistContinuation/An%20Ensemble%20Approach%20of%20Recurrent%20Neural%20Networks%20using%20Pre-Trained%20Embeddings%20for%20Playlist%20Completion.pdf) - [An Analysis of Approaches Taken in the ACM RecSys Challenge 2018 for Automatic Music Playlist Continuation](Industry/AutomaticPlaylistContinuation/An%20Analysis%20of%20Approaches%20Taken%20in%20the%20ACM%20RecSys%20Challenge%202018%20for%20Automatic%20Music%20Playlist%20Continuation.pdf) - [Automatic Music Playlist Continuation via Neighbor-based Collaborative Filtering and Discriminative Reweighting:Reranking](Industry/AutomaticPlaylistContinuation/Automatic%20Music%20Playlist%20Continuation%20via%20Neighbor-based%20Collaborative%20Filtering%20and%20Discriminative%20Reweighting%3AReranking.pdf) - [Artist-driven layering and user’s behaviour impact on recommendations in a playlist continuation scenario](Industry/AutomaticPlaylistContinuation/Artist-driven%20layering%20and%20user%E2%80%99s%20behaviour%20impact%20on%20recommendations%20in%20a%20playlist%20continuation%20scenario.pdf) - [Consistency-Aware Recommendation for User-Generated ItemList Continuation](Industry/AutomaticPlaylistContinuation/Consistency-Aware%20Recommendation%20for%20User-Generated%20ItemList%20Continuation.pdf) - [Dual-interest Factorization-heads Attention for Sequential Recommendation](Industry/AutomaticPlaylistContinuation/Dual-interest%20Factorization-heads%20Attention%20for%20Sequential%20Recommendation.pdf) - [Efficient Similarity Based Methods For The Playlist Continuation Task](Industry/AutomaticPlaylistContinuation/Efficient%20Similarity%20Based%20Methods%20For%20The%20Playlist%20Continuation%20Task.pdf) - [Efficient K-NN for Playlist Continuation](Industry/AutomaticPlaylistContinuation/Efficient%20K-NN%20for%20Playlist%20Continuation.pdf) - [Effective Nearest-Neighbor Music Recommendations](Industry/AutomaticPlaylistContinuation/Effective%20Nearest-Neighbor%20Music%20Recommendations.pdf) - [MMCF - Multimodal Collaborative Filtering for Automatic Playlist Continuation](Industry/AutomaticPlaylistContinuation/MMCF%20-%20Multimodal%20Collaborative%20Filtering%20for%20Automatic%20Playlist%20Continuation.pdf) - [MUSE - Music Recommender System with Shuffle Play Recommendation Enhancement](Industry/AutomaticPlaylistContinuation/MUSE%20-%20Music%20Recommender%20System%20with%20Shuffle%20Play%20Recommendation%20Enhancement.pdf) - [Offline Evaluation to Make Decisions About Playlist Recommendation Algorithms](Industry/AutomaticPlaylistContinuation/Offline%20Evaluation%20to%20Make%20Decisions%20About%20Playlist%20Recommendation%20Algorithms.pdf) - [Random Walk with Restart for Automatic Playlist Continuation and Query-Specific Adaptations](Industry/AutomaticPlaylistContinuation/Random%20Walk%20with%20Restart%20for%20Automatic%20Playlist%20Continuation%20and%20Query-Specific%20Adaptations.pdf) - [Social Tags and Emotions as main Features for the Next Song To Play in Automatic Playlist Continuation](Industry/AutomaticPlaylistContinuation/Social%20Tags%20and%20Emotions%20as%20main%20Features%20for%20the%20Next%20Song%20To%20Play%20in%20Automatic%20Playlist%20Continuation.pdf) - [TrailMix - An Ensemble Recommender System for Playlist Curation and Continuation](Industry/AutomaticPlaylistContinuation/TrailMix%20-%20An%20Ensemble%20Recommender%20System%20for%20Playlist%20Curation%20and%20Continuation.pdf) - [Towards Seed-Free Music Playlist Generation](Industry/AutomaticPlaylistContinuation/Towards%20Seed-Free%20Music%20Playlist%20Generation.pdf) - [Two-stage Model for Automatic Playlist Continuation at Scale](Industry/AutomaticPlaylistContinuation/Two-stage%20Model%20for%20Automatic%20Playlist%20Continuation%20at%20Scale.pdf) - [Using Adversarial Autoencoders for Multi-Modal Automatic Playlist Continuation](Industry/AutomaticPlaylistContinuation/Using%20Adversarial%20Autoencoders%20for%20Multi-Modal%20Automatic%20Playlist%20Continuation.pdf) - [User Recommendation in Content Curation Platforms](Industry/AutomaticPlaylistContinuation/User%20Recommendation%20in%20Content%20Curation%20Platforms.pdf) ## Pre-Rank - [[2020][Alibaba][COLD] COLD - Towards the Next Generation of Pre-Ranking System](Pre-Rank/%5B2020%5D%5BAlibaba%5D%5BCOLD%5D%20COLD%20-%20Towards%20the%20Next%20Generation%20of%20Pre-Ranking%20System.pdf) - [[2021][Alibaba] Towards a Better Tradeoff between Effectiveness and Efficiency in Pre-Ranking - A Learnable Feature Selection based Approach](Pre-Rank/%5B2021%5D%5BAlibaba%5D%20Towards%20a%20Better%20Tradeoff%20between%20Effectiveness%20and%20Efficiency%20in%20Pre-Ranking%20-%20A%20Learnable%20Feature%20Selection%20based%20Approach.pdf) - [[2022] On Ranking Consistency of Pre-ranking Stage](Pre-Rank/%5B2022%5D%20On%20Ranking%20Consistency%20of%20Pre-ranking%20Stage.pdf) - [[2022][Meituan] Contrastive Information Transfer for Pre-Ranking Systems](Pre-Rank/%5B2022%5D%5BMeituan%5D%20Contrastive%20Information%20Transfer%20for%20Pre-Ranking%20Systems.pdf) - [A Learnable Fully Interacted Two-Tower Model for Pre-Ranking System](Pre-Rank/A%20Learnable%20Fully%20Interacted%20Two-Tower%20Model%20for%20Pre-Ranking%20System.pdf) - [A Hybrid Cross-Stage Coordination Pre-ranking Model for Online Recommendation Systems](Pre-Rank/A%20Hybrid%20Cross-Stage%20Coordination%20Pre-ranking%20Model%20for%20Online%20Recommendation%20Systems.pdf) - [AutoFAS - Automatic Feature and Architecture Selection for Pre-Ranking System](Pre-Rank/AutoFAS%20-%20Automatic%20Feature%20and%20Architecture%20Selection%20for%20Pre-Ranking%20System.pdf) - [COPR - Consistency-Oriented Pre-Ranking for Online Advertising](Pre-Rank/COPR%20-%20Consistency-Oriented%20Pre-Ranking%20for%20Online%20Advertising.pdf) - [Cascade Ranking for Operational E-commerce Search](Pre-Rank/Cascade%20Ranking%20for%20Operational%20E-commerce%20Search.pdf) - [EENMF - An End-to-End Neural Matching Framework for E-Commerce Sponsored Search](Pre-Rank/EENMF%20-%20An%20End-to-End%20Neural%20Matching%20Framework%20for%20E-Commerce%20Sponsored%20Search.pdf) - [HIT Model - A Hierarchical Interaction-Enhanced Two-Tower Model for Pre-Ranking Systems](Pre-Rank/HIT%20Model%20-%20A%20Hierarchical%20Interaction-Enhanced%20Two-Tower%20Model%20for%20Pre-Ranking%20Systems.pdf) - [IntTower - the Next Generation of Two-Tower Model for Pre-Ranking System](Pre-Rank/IntTower%20-%20the%20Next%20Generation%20of%20Two-Tower%20Model%20for%20Pre-Ranking%20System.pdf) - [InteractRank - Personalized Web-Scale Search Pre-Ranking with Cross Interaction Features](Pre-Rank/InteractRank%20-%20Personalized%20Web-Scale%20Search%20Pre-Ranking%20with%20Cross%20Interaction%20Features.pdf) - [Rethinking the Role of Pre-ranking in Large-scale E-Commerce Searching System](Pre-Rank/Rethinking%20the%20Role%20of%20Pre-ranking%20in%20Large-scale%20E-Commerce%20Searching%20System.pdf) ## Re-Rank - [[2018][Hulu] Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity](Re-Rank/%5B2018%5D%5BHulu%5D%20Fast%20Greedy%20MAP%20Inference%20for%20Determinantal%20Point%20Process%20to%20Improve%20Recommendation%20Diversity.pdf) - [[2020][LinkedIn] Ads Allocation in Feed via Constrained Optimization](Re-Rank/%5B2020%5D%5BLinkedIn%5D%20Ads%20Allocation%20in%20Feed%20via%20Constrained%20Optimization.pdf) - [Beyond Pairwise Learning-To-Rank At Airbnb](Re-Rank/Beyond%20Pairwise%20Learning-To-Rank%20At%20Airbnb.pdf) - [Cross DQN - Cross Deep Q Network for Ads Allocation in Feed](Re-Rank/Cross%20DQN%20-%20Cross%20Deep%20Q%20Network%20for%20Ads%20Allocation%20in%20Feed.pdf) - [Controllable Multi-Objective Re-ranking with Policy Hypernetworks](Re-Rank/Controllable%20Multi-Objective%20Re-ranking%20with%20Policy%20Hypernetworks.pdf) - [Coverage, Redundancy and Size-Awareness in Genre Diversity for Recommender Systems](Re-Rank/Coverage%2C%20Redundancy%20and%20Size-Awareness%20in%20Genre%20Diversity%20for%20Recommender%20Systems.pdf) - [Discrete Conditional Diffusion for Reranking in Recommendation](Re-Rank/Discrete%20Conditional%20Diffusion%20for%20Reranking%20in%20Recommendation.pdf) - [DEAR - Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems](Re-Rank/DEAR%20-%20Deep%20Reinforcement%20Learning%20for%20Online%20Advertising%20Impression%20in%20Recommender%20Systems.pdf) - [Do Not Wait - Learning Re-Ranking Model Without User Feedback At Serving Time in E-Commerce](Re-Rank/Do%20Not%20Wait%20-%20Learning%20Re-Ranking%20Model%20Without%20User%20Feedback%20At%20Serving%20Time%20in%20E-Commerce.pdf) - [From Generation to Consumption - Personalized List Value Estimation for Re-ranking](Re-Rank/From%20Generation%20to%20Consumption%20-%20Personalized%20List%20Value%20Estimation%20for%20Re-ranking.pdf) - [GenDeR - A Generic Diversified Ranking Algorithm](Re-Rank/GenDeR%20-%20A%20Generic%20Diversified%20Ranking%20Algorithm.pdf) - [GRN - Generative Rerank Network for Context-wise Recommendation](Re-Rank/GRN%20-%20Generative%20Rerank%20Network%20for%20Context-wise%20Recommendation.pdf) - [Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search](Re-Rank/Globally%20Optimized%20Mutual%20Influence%20Aware%20Ranking%20in%20E-Commerce%20Search.pdf) - [Learning a Deep Listwise Context Model for Ranking Refinement](Re-Rank/Learning%20a%20Deep%20Listwise%20Context%20Model%20for%20Ranking%20Refinement.pdf) - [Multi-channel Integrated Recommendation with Exposure Constraints](Re-Rank/Multi-channel%20Integrated%20Recommendation%20with%20Exposure%20Constraints.pdf) - [Non-autoregressive Generative Models for Reranking Recommendation](Re-Rank/Non-autoregressive%20Generative%20Models%20for%20Reranking%20Recommendation.pdf) - [Neural Re-ranking in Multi-stage Recommender Systems - A Review](Re-Rank/Neural%20Re-ranking%20in%20Multi-stage%20Recommender%20Systems%20-%20A%20Review.pdf) - [Practical Diversified Recommendations on YouTube with Determinantal Point Processes](Re-Rank/Practical%20Diversified%20Recommendations%20on%20YouTube%20with%20Determinantal%20Point%20Processes.pdf) - [PIER - Permutation-Level Interest-Based End-to-End Re-ranking Framework in E-commerce](Re-Rank/PIER%20-%20Permutation-Level%20Interest-Based%20End-to-End%20Re-ranking%20Framework%20in%20E-commerce.pdf) - [Personalized Click Shaping through Lagrangian Duality for Online Recommendation](Re-Rank/Personalized%20Click%20Shaping%20through%20Lagrangian%20Duality%20for%20Online%20Recommendation.pdf) - [Personalized Re-ranking for Recommendation](Re-Rank/Personalized%20Re-ranking%20for%20Recommendation.pdf) - [Personalized Complementary Product Recommendation](Re-Rank/Personalized%20Complementary%20Product%20Recommendation.pdf) - [Personalized Re-ranking with Item Relationships for E-commerce](Re-Rank/Personalized%20Re-ranking%20with%20Item%20Relationships%20for%20E-commerce.pdf) - [Re-ranking With Constraints on Diversified Exposures for Homepage Recommender System](Re-Rank/Re-ranking%20With%20Constraints%20on%20Diversified%20Exposures%20for%20Homepage%20Recommender%20System.pdf) - [Revisit Recommender System in the Permutation Prospective](Re-Rank/Revisit%20Recommender%20System%20in%20the%20Permutation%20Prospective.pdf) - [SLATEQ - A Tractable Decomposition for Reinforcement Learning with Recommendation Sets](Re-Rank/SLATEQ%20-%20A%20Tractable%20Decomposition%20for%20Reinforcement%20Learning%20with%20Recommendation%20Sets.pdf) - [Seq2slate - Re-ranking and slate optimization with rnns](Re-Rank/Seq2slate%20-%20Re-ranking%20and%20slate%20optimization%20with%20rnns.pdf) - [The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries](Re-Rank/The%20Use%20of%20MMR%2C%20Diversity-Based%20Reranking%20for%20Reordering%20Documents%20and%20Producing%20Summaries.pdf) - [Towards Principled Learning for Re-ranking in Recommender Systems](Re-Rank/Towards%20Principled%20Learning%20for%20Re-ranking%20in%20Recommender%20Systems.pdf) - [User Response Models to Improve a REINFORCE Recommender System](Re-Rank/User%20Response%20Models%20to%20Improve%20a%20REINFORCE%20Recommender%20System.pdf) ## Match - [[2015][Microsoft][DSSM in Recsys] A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems](Match/%5B2015%5D%5BMicrosoft%5D%5BDSSM%20in%20Recsys%5D%20A%20Multi-View%20Deep%20Learning%20Approach%20for%20Cross%20Domain%20User%20Modeling%20in%20Recommendation%20Systems.pdf) - [[2015][Sceptre] Inferring Networks of Substitutable and Complementary Products](Match/%5B2015%5D%5BSceptre%5D%20Inferring%20Networks%20of%20Substitutable%20and%20Complementary%20Products.pdf) - [[2016][Yahoo][App2Vec] App2Vec - Vector Modeling of Mobile Apps and Applications](Match/%5B2016%5D%5BYahoo%5D%5BApp2Vec%5D%20App2Vec%20-%20Vector%20Modeling%20of%20Mobile%20Apps%20and%20Applications.pdf) - [[2018][TC-CML] Loss Aversion in Recommender Systems - Utilizing Negative User Preference to Improve Recommendation Quality](Match/%5B2018%5D%5BTC-CML%5D%20Loss%20Aversion%20in%20Recommender%20Systems%20-%20Utilizing%20Negative%20User%20Preference%20to%20Improve%20Recommendation%20Quality.pdf) - [[2019][Google] Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations](Match/%5B2019%5D%5BGoogle%5D%20Sampling-Bias-Corrected%20Neural%20Modeling%20for%20Large%20Corpus%20Item%20Recommendations.pdf) - [[2019][Baidu][MOBIUS] MOBIUS - Towards the Next Generation of Query-Ad Matching in Baidu’s Sponsored Search](Match/%5B2019%5D%5BBaidu%5D%5BMOBIUS%5D%20MOBIUS%20-%20Towards%20the%20Next%20Generation%20of%20Query-Ad%20Matching%20in%20Baidu%E2%80%99s%20Sponsored%20Search.pdf) - [[2019][Alibaba][SDM] SDM - Sequential Deep Matching Model for Online Large-scale Recommender System](Match/%5B2019%5D%5BAlibaba%5D%5BSDM%5D%20SDM%20-%20Sequential%20Deep%20Matching%20Model%20for%20Online%20Large-scale%20Recommender%20System.pdf) - [[2020][Baidu] Sample Optimization For Display Advertising](Match/%5B2020%5D%5BBaidu%5D%20Sample%20Optimization%20For%20Display%20Advertising.pdf) - [[2020][Alibaba][Swing&Surprise] Large Scale Product Graph Construction for Recommendation in E-commerce](Match/%5B2020%5D%5BAlibaba%5D%5BSwing%26Surprise%5D%20Large%20Scale%20Product%20Graph%20Construction%20for%20Recommendation%20in%20E-commerce.pdf) - [[2020][Weixin][UTPM] Learning to Build User-tag Profile in Recommendation System](Match/%5B2020%5D%5BWeixin%5D%5BUTPM%5D%20Learning%20to%20Build%20User-tag%20Profile%20in%20Recommendation%20System.pdf) - [[2020][Facebook][EBR] Embedding-based Retrieval in Facebook Search](Match/%5B2020%5D%5BFacebook%5D%5BEBR%5D%20Embedding-based%20Retrieval%20in%20Facebook%20Search.pdf) - [[2020][Google][MNS] Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations](Match/%5B2020%5D%5BGoogle%5D%5BMNS%5D%20Mixed%20Negative%20Sampling%20for%20Learning%20Two-tower%20Neural%20Networks%20in%20Recommendations.pdf) - [[2021][Google] Self-supervised Learning for Large-scale Item Recommendations](Match/%5B2021%5D%5BGoogle%5D%20Self-supervised%20Learning%20for%20Large-scale%20Item%20Recommendations.pdf) - [[2021][Alibaba][MGDSPR] Embedding-based Product Retrieval in Taobao Search](Match/%5B2021%5D%5BAlibaba%5D%5BMGDSPR%5D%20Embedding-based%20Product%20Retrieval%20in%20Taobao%20Search.pdf) - [[2021][Alibaba][XDM] XDM - Improving Sequential Deep Matching with Unclicked User Behaviors for Recommender System](Match/%5B2021%5D%5BAlibaba%5D%5BXDM%5D%20XDM%20-%20Improving%20Sequential%20Deep%20Matching%20with%20Unclicked%20User%20Behaviors%20for%20Recommender%20System.pdf) - [[2023] Adap-tau - Adaptively Modulating Embedding Magnitude for Recommendation](Match/%5B2023%5D%20Adap-tau%20-%20Adaptively%20Modulating%20Embedding%20Magnitude%20for%20Recommendation.pdf) - [[2023][JD][MMSE] Learning Multi-Stage Multi-Grained Semantic Embeddings for E-Commerce Search](Match/%5B2023%5D%5BJD%5D%5BMMSE%5D%20Learning%20Multi-Stage%20Multi-Grained%20Semantic%20Embeddings%20for%20E-Commerce%20Search.pdf) - [Attentive Collaborative Filtering - Multimedia Recommendation with Item- and Component-Level A‚ention](Match/Attentive%20Collaborative%20Filtering%20-%20Multimedia%20Recommendation%20with%20Item-%20and%20Component-Level%20A%C2%82ention.pdf) - [Attentive Sequential Models of Latent Intent for Next Item Recommendation](Match/Attentive%20Sequential%20Models%20of%20Latent%20Intent%20for%20Next%20Item%20Recommendation.pdf) - [A User-Centered Concept Mining System for Query and Document Understanding at Tencent](Match/A%20User-Centered%20Concept%20Mining%20System%20for%20Query%20and%20Document%20Understanding%20at%20Tencent.pdf) - [An Empirical Study of Selection Bias in Pinterest Ads Retrieval](Match/An%20Empirical%20Study%20of%20Selection%20Bias%20in%20Pinterest%20Ads%20Retrieval.pdf) - [AutoRec - Autoencoders Meet Collaborative Filtering](Match/AutoRec%20-%20Autoencoders%20Meet%20Collaborative%20Filtering.pdf) - [A Simple Convolutional Generative Network for Next Item Recommendation](Match/A%20Simple%20Convolutional%20Generative%20Network%20for%20Next%20Item%20Recommendation.pdf) - [A Dual Augmented Two-tower Model for Online Large-scale Recommendation](Match/A%20Dual%20Augmented%20Two-tower%20Model%20for%20Online%20Large-scale%20Recommendation.pdf) - [Binary Embedding-based Retrieval at Tencent](Match/Binary%20Embedding-based%20Retrieval%20at%20Tencent.pdf) - [Beyond Two-Tower Matching - Learning Sparse Retrievable Cross-Interactions for Recommendation](Match/Beyond%20Two-Tower%20Matching%20-%20Learning%20Sparse%20Retrievable%20Cross-Interactions%20for%20Recommendation.pdf) - [Build Faster with Less - A Journey to Accelerate Sparse Model Building for Semantic Matching in Product Search](Match/Build%20Faster%20with%20Less%20-%20A%20Journey%20to%20Accelerate%20Sparse%20Model%20Building%20for%20Semantic%20Matching%20in%20Product%20Search.pdf) - [Bootstrapping Conditional Retrieval for User-to-Item Recommendations](Match/Bootstrapping%20Conditional%20Retrieval%20for%20User-to-Item%20Recommendations.pdf) - [Beyond Semantics - Learning a Behavior Augmented Relevance Model with Self-supervised Learning](Match/Beyond%20Semantics%20-%20Learning%20a%20Behavior%20Augmented%20Relevance%20Model%20with%20Self-supervised%20Learning.pdf) - [Balancing Semantic Relevance and Engagement in Related Video Recommendations](Match/Balancing%20Semantic%20Relevance%20and%20Engagement%20in%20Related%20Video%20Recommendations.pdf) - [CROLoss - Towards a Customizable Loss for Retrieval Models in Recommender Systems](Match/CROLoss%20-%20Towards%20a%20Customizable%20Loss%20for%20Retrieval%20Models%20in%20Recommender%20Systems.pdf) - [Collaborative Denoising Auto-Encoders for Top-N Recommender Systems](Match/Collaborative%20Denoising%20Auto-Encoders%20for%20Top-N%20Recommender%20Systems.pdf) - [Coarse-to-Fine Sparse Sequential Recommendation](Match/Coarse-to-Fine%20Sparse%20Sequential%20Recommendation.pdf) - [Cross-Batch Negative Sampling for Training Two-Tower Recommenders](Match/Cross-Batch%20Negative%20Sampling%20for%20Training%20Two-Tower%20Recommenders.pdf) - [CSMF - Cascaded Selective Mask Fine-Tuning for Multi-Objective Embedding-Based Retrieval](Match/CSMF%20-%20Cascaded%20Selective%20Mask%20Fine-Tuning%20for%20Multi-Objective%20Embedding-Based%20Retrieval.pdf) - [Collaborative Deep Learning for Recommender Systems](Match/Collaborative%20Deep%20Learning%20for%20Recommender%20Systems.pdf) - [CRM - Retrieval Model with Controllable Condition](Match/CRM%20-%20Retrieval%20Model%20with%20Controllable%20Condition.pdf) - [CroPS - Improving Dense Retrieval with Cross-Perspective Positive Samples in Short-Video Search](Match/CroPS%20-%20Improving%20Dense%20Retrieval%20with%20Cross-Perspective%20Positive%20Samples%20in%20Short-Video%20Search.pdf) - [Deep Matrix Factorization Models for Recommender Systems](Match/Deep%20Matrix%20Factorization%20Models%20for%20Recommender%20Systems.pdf) - [Divide and Conquer - Towards Better Embedding-based Retrieval for Recommender Systems from a Multi-task Perspective](Match/Divide%20and%20Conquer%20-%20Towards%20Better%20Embedding-based%20Retrieval%20for%20Recommender%20Systems%20from%20a%20Multi-task%20Perspective.pdf) - [Disentangled Self-Supervision in Sequential Recommenders](Match/Disentangled%20Self-Supervision%20in%20Sequential%20Recommenders.pdf) - [Deep Collaborative Filtering via Marginalized Denoising Auto-encoder](Match/Deep%20Collaborative%20Filtering%20via%20Marginalized%20Denoising%20Auto-encoder.pdf) - [Dynamic Multi-Behavior Sequence Modeling for Next Item Recommendation](Match/Dynamic%20Multi-Behavior%20Sequence%20Modeling%20for%20Next%20Item%20Recommendation.pdf) - [ESANS - Effective and Semantic-Aware Negative Sampling for Large-Scale Retrieval Systems](Match/ESANS%20-%20Effective%20and%20Semantic-Aware%20Negative%20Sampling%20for%20Large-Scale%20Retrieval%20Systems.pdf) - [Efficient Training on Very Large Corpora via Gramian Estimation](Match/Efficient%20Training%20on%20Very%20Large%20Corpora%20via%20Gramian%20Estimation.pdf) - [Extreme Multi-label Learning for Semantic Matching in Product Search](Match/Extreme%20Multi-label%20Learning%20for%20Semantic%20Matching%20in%20Product%20Search.pdf) - [Embedding-based Retrieval in Multimodal Content Moderation](Match/Embedding-based%20Retrieval%20in%20Multimodal%20Content%20Moderation.pdf) - [Factorization Meets the Neighborhood - a Multifaceted Collaborative Filtering Model](Match/Factorization%20Meets%20the%20Neighborhood%20-%20a%20Multifaceted%20Collaborative%20Filtering%20Model.pdf) - [Fast Matrix Factorization for Online Recommendation with Implicit Feedback](Match/Fast%20Matrix%20Factorization%20for%20Online%20Recommendation%20with%20Implicit%20Feedback.pdf) - [HeterRec - Heterogeneous Information Transformer for Scalable Sequential Recommendation](Match/HeterRec%20-%20Heterogeneous%20Information%20Transformer%20for%20Scalable%20Sequential%20Recommendation.pdf) - [Hierarchical Structured Neural Network- Efficient Retrieval Scaling for Large Scale Recommendation](Match/Hierarchical%20Structured%20Neural%20Network-%20Efficient%20Retrieval%20Scaling%20for%20Large%20Scale%20Recommendation.pdf) - [Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems](Match/Hierarchical%20Temporal%20Convolutional%20Networks%20for%20Dynamic%20Recommender%20Systems.pdf) - [Heterogeneous Graph Neural Networks for Large-Scale Bid Keyword Matching](Match/Heterogeneous%20Graph%20Neural%20Networks%20for%20Large-Scale%20Bid%20Keyword%20Matching.pdf) - [Improving Ad matching via Cluster-Adaptive Keyword Expansion and Relevance tuning](Match/Improving%20Ad%20matching%20via%20Cluster-Adaptive%20Keyword%20Expansion%20and%20Relevance%20tuning.pdf) - [Itinerary-aware Personalized Deep Matching at Fliggy](Match/Itinerary-aware%20Personalized%20Deep%20Matching%20at%20Fliggy.pdf) - [I^3 Retriever- Incorporating Implicit Interaction in Pre-trained Language Models for Passage Retrieval](Match/I%5E3%20Retriever-%20Incorporating%20Implicit%20Interaction%20in%20Pre-trained%20Language%20Models%20for%20Passage%20Retrieval.pdf) - [Improving Recommendation Accuracy using Networks of Substitutable and Complementary Products](Match/Improving%20Recommendation%20Accuracy%20using%20Networks%20of%20Substitutable%20and%20Complementary%20Products.pdf) - [ItemSage - Learning Product Embeddings for Shopping Recommendations at Pinterest](Match/ItemSage%20-%20Learning%20Product%20Embeddings%20for%20Shopping%20Recommendations%20at%20Pinterest.pdf) - [KuaiFormer - Transformer-Based Retrieval at Kuaishou](Match/KuaiFormer%20-%20Transformer-Based%20Retrieval%20at%20Kuaishou.pdf) - [Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking](Match/Latent%20Relational%20Metric%20Learning%20via%20Memory-based%20Attention%20for%20Collaborative%20Ranking.pdf) - [Learning from History and Present - Next-item Recommendation via Discriminatively Exploiting User Behaviors](Match/Learning%20from%20History%20and%20Present%20-%20Next-item%20Recommendation%20via%20Discriminatively%20Exploiting%20User%20Behaviors.pdf) - [Learning Deep Structured Semantic Models for Web Search using Clickthrough Data](Match/Learning%20Deep%20Structured%20Semantic%20Models%20for%20Web%20Search%20using%20Clickthrough%20Data.pdf) - [Locker - Locally Constrained Self-Attentive Sequential Recommendation](Match/Locker%20-%20Locally%20Constrained%20Self-Attentive%20Sequential%20Recommendation.pdf) - [LongRetriever - Towards Ultra-Long Sequence based Candidate Retrieval for Recommendation](Match/LongRetriever%20-%20Towards%20Ultra-Long%20Sequence%20based%20Candidate%20Retrieval%20for%20Recommendation.pdf) - [Multi-Objective Personalized Product Retrieval in Taobao Search](Match/Multi-Objective%20Personalized%20Product%20Retrieval%20in%20Taobao%20Search.pdf) - [M5 - Multi-Modal Multi-Interest Multi-Scenario Matching for Over-the-Top Recommendation](Match/M5%20-%20Multi-Modal%20Multi-Interest%20Multi-Scenario%20Matching%20for%20Over-the-Top%20Recommendation.pdf) - [Modeling Dynamic Missingness of Implicit Feedback for Recommendation](Match/Modeling%20Dynamic%20Missingness%20of%20Implicit%20Feedback%20for%20Recommendation.pdf) - [MV-HAN - A Hybrid Attentive Networks based Multi-View Learning Model for Large-scale Contents Recommendation](Match/MV-HAN%20-%20A%20Hybrid%20Attentive%20Networks%20based%20Multi-View%20Learning%20Model%20for%20Large-scale%20Contents%20Recommendation.pdf) - [Neural Attentive Session-based Recommendation](Match/Neural%20Attentive%20Session-based%20Recommendation.pdf) - [Neighborhood-based Hard Negative Mining for Sequential Recommendation](Match/Neighborhood-based%20Hard%20Negative%20Mining%20for%20Sequential%20Recommendation.pdf) - [NAIS - Neural Attentive Item Similarity Model for Recommendation](Match/NAIS%20-%20Neural%20Attentive%20Item%20Similarity%20Model%20for%20Recommendation.pdf) - [Octopus - Comprehensive and Elastic User Representation for the Generation of Recommendation Candidates](Match/Octopus%20-%20Comprehensive%20and%20Elastic%20User%20Representation%20for%20the%20Generation%20of%20Recommendation%20Candidates.pdf) - [Optimizing Recall or Relevance? A Multi-Task Multi-Head Approach for Item-to-Item Retrieval in Recommendation](Match/Optimizing%20Recall%20or%20Relevance%3F%20A%20Multi-Task%20Multi-Head%20Approach%20for%20Item-to-Item%20Retrieval%20in%20Recommendation.pdf) - [Outer Product-based Neural Collaborative Filtering](Match/Outer%20Product-based%20Neural%20Collaborative%20Filtering.pdf) - [On the Theories Behind Hard Negative Sampling for Recommendation](Match/On%20the%20Theories%20Behind%20Hard%20Negative%20Sampling%20for%20Recommendation.pdf) - [On Sampling Strategies for Neural Network-based Collaborative Filtering](Match/On%20Sampling%20Strategies%20for%20Neural%20Network-based%20Collaborative%20Filtering.pdf) - [PI2I - A Personalized Item-Based Collaborative Filtering Retrieval Framework](Match/PI2I%20-%20A%20Personalized%20Item-Based%20Collaborative%20Filtering%20Retrieval%20Framework.pdf) - [PinnerSage - Multi-Modal User Embedding Framework for Recommendations at Pinterest](Match/PinnerSage%20-%20Multi-Modal%20User%20Embedding%20Framework%20for%20Recommendations%20at%20Pinterest.pdf) - [Pre-training Tasks for User Intent Detection and Embedding Retrieval in E-commerce Search](Match/Pre-training%20Tasks%20for%20User%20Intent%20Detection%20and%20Embedding%20Retrieval%20in%20E-commerce%20Search.pdf) - [Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding](Match/Personalized%20Top-N%20Sequential%20Recommendation%20via%20Convolutional%20Sequence%20Embedding.pdf) - [Path-based Deep Network for Candidate Item Matching in Recommenders](Match/Path-based%20Deep%20Network%20for%20Candidate%20Item%20Matching%20in%20Recommenders.pdf) - [Que2Search - Fast and Accurate Query and Document Understanding for Search at Facebook](Match/Que2Search%20-%20Fast%20and%20Accurate%20Query%20and%20Document%20Understanding%20for%20Search%20at%20Facebook.pdf) - [Que2Engage - Embedding-based Retrieval for Relevant and Engaging Products at Facebook Marketplace](Match/Que2Engage%20-%20Embedding-based%20Retrieval%20for%20Relevant%20and%20Engaging%20Products%20at%20Facebook%20Marketplace.pdf) - [Recommender Systems with Generative Retrieval](Match/Recommender%20Systems%20with%20Generative%20Retrieval.pdf) - [Representing and Recommending Shopping Baskets with Complementarity, Compatibility, and Loyalty](Match/Representing%20and%20Recommending%20Shopping%20Baskets%20with%20Complementarity%2C%20Compatibility%2C%20and%20Loyalty.pdf) - [Robust Representation Learning for Unified Online Top-K Recommendation](Match/Robust%20Representation%20Learning%20for%20Unified%20Online%20Top-K%20Recommendation.pdf) - [Real-time Indexing for Large-scale Recommendation by Streaming Vector Quantization Retriever](Match/Real-time%20Indexing%20for%20Large-scale%20Recommendation%20by%20Streaming%20Vector%20Quantization%20Retriever.pdf) - [RADAR - Recall Augmentation through Deferred Asynchronous Retrieval](Match/RADAR%20-%20Recall%20Augmentation%20through%20Deferred%20Asynchronous%20Retrieval.pdf) - [Revisiting Neural Retrieval on Accelerators](Match/Revisiting%20Neural%20Retrieval%20on%20Accelerators.pdf) - [Retrieval with Learned Similarities](Match/Retrieval%20with%20Learned%20Similarities.pdf) - [Recommendation on Live - Streaming Platforms- Dynamic Availability and Repeat Consumption](Match/Recommendation%20on%20Live%20-%20Streaming%20Platforms-%20Dynamic%20Availability%20and%20Repeat%20Consumption.pdf) - [Sequential Recommender System based on Hierarchical Attention Network](Match/Sequential%20Recommender%20System%20based%20on%20Hierarchical%20Attention%20Network.pdf) - [Sequential Recommendation via Stochastic Self-Attention](Match/Sequential%20Recommendation%20via%20Stochastic%20Self-Attention.pdf) - [Semi-supervised Adversarial Learning for Complementary Item Recommendation](Match/Semi-supervised%20Adversarial%20Learning%20for%20Complementary%20Item%20Recommendation.pdf) - [Sparse-Interest Network for Sequential Recommendation](Match/Sparse-Interest%20Network%20for%20Sequential%20Recommendation.pdf) - [Synergizing Implicit and Explicit User Interests - A Multi-Embedding Retrieval Framework at Pinterest](Match/Synergizing%20Implicit%20and%20Explicit%20User%20Interests%20-%20A%20Multi-Embedding%20Retrieval%20Framework%20at%20Pinterest.pdf) - [Self-Attentive Sequential Recommendation](Match/Self-Attentive%20Sequential%20Recommendation.pdf) - [StarSpace - Embed All The Things!](Match/StarSpace%20-%20Embed%20All%20The%20Things%21.pdf) - [SPM - Structured Pretraining and Matching Architectures for Relevance Modeling in Meituan Search](Match/SPM%20-%20Structured%20Pretraining%20and%20Matching%20Architectures%20for%20Relevance%20Modeling%20in%20Meituan%20Search.pdf) - [Suggest, complement, inspire - story of Two Tower recommendations at Allegro.com](Match/Suggest%2C%20complement%2C%20inspire%20-%20story%20of%20Two%20Tower%20recommendations%20at%20Allegro.com.pdf) - [SimpleX - A Simple and Strong Baseline for Collaborative Filtering](Match/SimpleX%20-%20A%20Simple%20and%20Strong%20Baseline%20for%20Collaborative%20Filtering.pdf) - [Towards Automated Negative Sampling in Implicit Recommendation](Match/Towards%20Automated%20Negative%20Sampling%20in%20Implicit%20Recommendation.pdf) - [Towards Personalized and Semantic Retrieval - An End-to-End Solution for E-commerce Search via Embedding Learning](Match/Towards%20Personalized%20and%20Semantic%20Retrieval%20-%20An%20End-to-End%20Solution%20for%20E-commerce%20Search%20via%20Embedding%20Learning.pdf) - [Unified Generative & Dense Retrieval for Query Rewriting in Sponsored Search](Match/Unified%20Generative%20%26%20Dense%20Retrieval%20for%20Query%20Rewriting%20in%20Sponsored%20Search.pdf) - [Uni-Retriever - Towards Learning The Unified Embedding Based Retriever in Bing Sponsored Search](Match/Uni-Retriever%20-%20Towards%20Learning%20The%20Unified%20Embedding%20Based%20Retriever%20in%20Bing%20Sponsored%20Search.pdf) - [User Long-Term Multi-Interest Retrieval Model for Recommendation](Match/User%20Long-Term%20Multi-Interest%20Retrieval%20Model%20for%20Recommendation.pdf) - [Unifying Generative and Dense Retrieval for Sequential Recommendation](Match/Unifying%20Generative%20and%20Dense%20Retrieval%20for%20Sequential%20Recommendation.pdf) - [VIER - Visual Imagination Enhanced Retrieval in Sponsored Search](Match/VIER%20-%20Visual%20Imagination%20Enhanced%20Retrieval%20in%20Sponsored%20Search.pdf) - [Variational Autoencoders for Collaborative Filtering](Match/Variational%20Autoencoders%20for%20Collaborative%20Filtering.pdf) - [gSASRec - Reducing Overconfidence in Sequential Recommendation Trained with Negative Sampling](Match/gSASRec%20-%20Reducing%20Overconfidence%20in%20Sequential%20Recommendation%20Trained%20with%20Negative%20Sampling.pdf) #### Mulit-Interest - [[2019][Alibaba][MIND] Multi-Interest Network with Dynamic Routing for Recommendation at Tmall](Match/Mulit-Interest/%5B2019%5D%5BAlibaba%5D%5BMIND%5D%20Multi-Interest%20Network%20with%20Dynamic%20Routing%20for%20Recommendation%20at%20Tmall.pdf) - [[2020][Alibaba][ComiRec] Controllable Multi-Interest Framework for Recommendation](Match/Mulit-Interest/%5B2020%5D%5BAlibaba%5D%5BComiRec%5D%20Controllable%20Multi-Interest%20Framework%20for%20Recommendation.pdf) - [Attribute Simulation for Item Embedding Enhancement in Multi-interest Recommendation](Match/Mulit-Interest/Attribute%20Simulation%20for%20Item%20Embedding%20Enhancement%20in%20Multi-interest%20Recommendation.pdf) - [Density Weighting for Multi-Interest Personalized Recommendation](Match/Mulit-Interest/Density%20Weighting%20for%20Multi-Interest%20Personalized%20Recommendation.pdf) - [Every Preference Changes Differently - Neural Multi-Interest Preference Model with Temporal Dynamics for Recommendation](Match/Mulit-Interest/Every%20Preference%20Changes%20Differently%20-%20Neural%20Multi-Interest%20Preference%20Model%20with%20Temporal%20Dynamics%20for%20Recommendation.pdf) - [Improving Multi-Interest Network with Stable Learning](Match/Mulit-Interest/Improving%20Multi-Interest%20Network%20with%20Stable%20Learning.pdf) - [Multiple Interest and Fine Granularity Network for User Modeling](Match/Mulit-Interest/Multiple%20Interest%20and%20Fine%20Granularity%20Network%20for%20User%20Modeling.pdf) #### Tree-Based - [Deep Retrieval - Learning A Retrievable Structure for Large-Scale Recommendations](Match/Tree-Based/Deep%20Retrieval%20-%20Learning%20A%20Retrievable%20Structure%20for%20Large-Scale%20Recommendations.pdf) - [Joint Optimization of Tree-based Index and Deep Model for Recommender Systems](Match/Tree-Based/Joint%20Optimization%20of%20Tree-based%20Index%20and%20Deep%20Model%20for%20Recommender%20Systems.pdf) - [Learning Optimal Tree Models under Beam Search](Match/Tree-Based/Learning%20Optimal%20Tree%20Models%20under%20Beam%20Search.pdf) - [Learning Tree-based Deep Model for Recommender Systems](Match/Tree-Based/Learning%20Tree-based%20Deep%20Model%20for%20Recommender%20Systems.pdf) #### ANN - [Approximate Nearest Neighbor Search under Neural Similarity Metric for Large-Scale Recommendation](Match/ANN/Approximate%20Nearest%20Neighbor%20Search%20under%20Neural%20Similarity%20Metric%20for%20Large-Scale%20Recommendation.pdf) - [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](Match/ANN/Efficient%20and%20robust%20approximate%20nearest%20neighbor%20search%20using%20Hierarchical%20Navigable%20Small%20World%20graphs.pdf) #### Nearline - [Truncation-Free Matching System for Display Advertising at Alibaba](Match/Nearline/Truncation-Free%20Matching%20System%20for%20Display%20Advertising%20at%20Alibaba.pdf) #### Classic - [Collaborative Filtering Recommender Systems](Match/Classic/Collaborative%20Filtering%20Recommender%20Systems.pdf) - [GroupLens - An open architecture for collaborative filtering of Netnews](Match/Classic/GroupLens%20-%20An%20open%20architecture%20for%20collaborative%20filtering%20of%20Netnews.pdf) - [Item-Based Collaborative Filtering Recommendation Algorithms](Match/Classic/Item-Based%20Collaborative%20Filtering%20Recommendation%20Algorithms.pdf) - [MatchSim - a novel similarity measure based on maximum neighborhood matching](Match/Classic/MatchSim%20-%20a%20novel%20similarity%20measure%20based%20on%20maximum%20neighborhood%20matching.pdf) #### GNN - [[2014][word2vec] Negative-Sampling Word-Embedding Method](Match/GNN/%5B2014%5D%5Bword2vec%5D%20Negative-Sampling%20Word-Embedding%20Method.pdf) - [[2014][DeepWalk] DeepWalk - Online Learning of Social Representations](Match/GNN/%5B2014%5D%5BDeepWalk%5D%20DeepWalk%20-%20Online%20Learning%20of%20Social%20Representations.pdf) - [[2015][Microsoft][LINE] LINE - Large-scale Information Network Embedding](Match/GNN/%5B2015%5D%5BMicrosoft%5D%5BLINE%5D%20LINE%20-%20Large-scale%20Information%20Network%20Embedding.pdf) - [[2016][SDNE] Structural Deep Network Embedding](Match/GNN/%5B2016%5D%5BSDNE%5D%20Structural%20Deep%20Network%20Embedding.pdf) - [[2016][Stanford][node2vec] node2vec - Scalable Feature Learning for Networks](Match/GNN/%5B2016%5D%5BStanford%5D%5Bnode2vec%5D%20node2vec%20-%20Scalable%20Feature%20Learning%20for%20Networks.pdf) - [[2016][word2vec] word2vec Parameter Learning Explained](Match/GNN/%5B2016%5D%5Bword2vec%5D%20word2vec%20Parameter%20Learning%20Explained.pdf) - [[2016][item2vec] ITEM2VEC - NEURAL ITEM EMBEDDING FOR COLLABORATIVE FILTERING](Match/GNN/%5B2016%5D%5Bitem2vec%5D%20ITEM2VEC%20-%20NEURAL%20ITEM%20EMBEDDING%20FOR%20COLLABORATIVE%20FILTERING.pdf) - [[2017][GCN] SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS](Match/GNN/%5B2017%5D%5BGCN%5D%20SEMI-SUPERVISED%20CLASSIFICATION%20WITH%20GRAPH%20CONVOLUTIONAL%20NETWORKS.pdf) - [[2017][Stanford][GraphSage] Inductive Representation Learning on Large Graphs](Match/GNN/%5B2017%5D%5BStanford%5D%5BGraphSage%5D%20Inductive%20Representation%20Learning%20on%20Large%20Graphs.pdf) - [[2018][GAT] GRAPH ATTENTION NETWORKS](Match/GNN/%5B2018%5D%5BGAT%5D%20GRAPH%20ATTENTION%20NETWORKS.pdf) - [[2018][Alibaba] Learning and Transferring IDs Representation in E-commerce](Match/GNN/%5B2018%5D%5BAlibaba%5D%20Learning%20and%20Transferring%20IDs%20Representation%20in%20E-commerce.pdf) - [[2018][Pinterest][PinSage] Graph Convolutional Neural Networks for Web-Scale Recommender Systems](Match/GNN/%5B2018%5D%5BPinterest%5D%5BPinSage%5D%20Graph%20Convolutional%20Neural%20Networks%20for%20Web-Scale%20Recommender%20Systems.pdf) - [[2018][Etsy] Learning Item-Interaction Embeddings for User Recommendations](Match/GNN/%5B2018%5D%5BEtsy%5D%20Learning%20Item-Interaction%20Embeddings%20for%20User%20Recommendations.pdf) - [[2018][Alibaba][EGES] Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba](Match/GNN/%5B2018%5D%5BAlibaba%5D%5BEGES%5D%20Billion-scale%20Commodity%20Embedding%20for%20E-commerce%20Recommendation%20in%20Alibaba.pdf) - [[2019][SR-GNN] Session-based Recommendation with Graph Neural Networks](Match/GNN/%5B2019%5D%5BSR-GNN%5D%20Session-based%20Recommendation%20with%20Graph%20Neural%20Networks.pdf) - [[2019][NGCF] Neural Graph Collaborative Filtering](Match/GNN/%5B2019%5D%5BNGCF%5D%20Neural%20Graph%20Collaborative%20Filtering.pdf) - [[2020][LightGCN] LightGCN - Simplifying and Powering Graph Convolution Network for Recommendation](Match/GNN/%5B2020%5D%5BLightGCN%5D%20LightGCN%20-%20Simplifying%20and%20Powering%20Graph%20Convolution%20Network%20for%20Recommendation.pdf) - [ATBRG - Adaptive Target-Behavior Relational Graph Network for Effective Recommendation](Match/GNN/ATBRG%20-%20Adaptive%20Target-Behavior%20Relational%20Graph%20Network%20for%20Effective%20Recommendation.pdf) - [A Survey of Graph Neural Networks for Social Recommender Systems](Match/GNN/A%20Survey%20of%20Graph%20Neural%20Networks%20for%20Social%20Recommender%20Systems.pdf) - [Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View](Match/GNN/Attentional%20Graph%20Convolutional%20Networks%20for%20Knowledge%20Concept%20Recommendation%20in%20MOOCs%20in%20a%20Heterogeneous%20View.pdf) - [Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer](Match/GNN/Continuous-Time%20Sequential%20Recommendation%20with%20Temporal%20Graph%20Collaborative%20Transformer.pdf) - [Compressed Interaction Graph based Framework for Multi-behavior Recommendation](Match/GNN/Compressed%20Interaction%20Graph%20based%20Framework%20for%20Multi-behavior%20Recommendation.pdf) - [Debiasing Neighbor Aggregation for Graph Neural Network in Recommender Systems](Match/GNN/Debiasing%20Neighbor%20Aggregation%20for%20Graph%20Neural%20Network%20in%20Recommender%20Systems.pdf) - [DC-GNN - Decoupled Graph Neural Networks for Improving and Accelerating Large-Scale E-commerce Retrieval](Match/GNN/DC-GNN%20-%20Decoupled%20Graph%20Neural%20Networks%20for%20Improving%20and%20Accelerating%20Large-Scale%20E-commerce%20Retrieval.pdf) - [Disentangled Graph Collaborative Filtering](Match/GNN/Disentangled%20Graph%20Collaborative%20Filtering.pdf) - [Decoupled Graph Convolution Network for Inferring Substitutable and Complementary Items](Match/GNN/Decoupled%20Graph%20Convolution%20Network%20for%20Inferring%20Substitutable%20and%20Complementary%20Items.pdf) - [Embedding-based News Recommendationfor Millions of Users](Match/GNN/Embedding-based%20News%20Recommendationfor%20Millions%20of%20Users.pdf) - [Explicit Semantic Cross Feature Learning via Pre-trained Graph Neural Networks for CTR Prediction](Match/GNN/Explicit%20Semantic%20Cross%20Feature%20Learning%20via%20Pre-trained%20Graph%20Neural%20Networks%20for%20CTR%20Prediction.pdf) - [Enhancing Catalog Relationship Problems with Heterogeneous Graphs and Graph Neural Networks Distillation](Match/GNN/Enhancing%20Catalog%20Relationship%20Problems%20with%20Heterogeneous%20Graphs%20and%20Graph%20Neural%20Networks%20Distillation.pdf) - [E-commerce Search via Content Collaborative Graph Neural Network](Match/GNN/E-commerce%20Search%20via%20Content%20Collaborative%20Graph%20Neural%20Network.pdf) - [Friend Recommendations with Self-Rescaling Graph Neural Networks](Match/GNN/Friend%20Recommendations%20with%20Self-Rescaling%20Graph%20Neural%20Networks.pdf) - [FASTGCN - FAST LEARNING WITH GRAPH CONVOLUTIONAL NETWORKS VIA IMPORTANCE SAMPLING](Match/GNN/FASTGCN%20-%20FAST%20LEARNING%20WITH%20GRAPH%20CONVOLUTIONAL%20NETWORKS%20VIA%20IMPORTANCE%20SAMPLING.pdf) - [Graph Convolutional Matrix Completion](Match/GNN/Graph%20Convolutional%20Matrix%20Completion.pdf) - [Graph Neural Networks for Friend Ranking in Large-scale Social Platforms](Match/GNN/Graph%20Neural%20Networks%20for%20Friend%20Ranking%20in%20Large-scale%20Social%20Platforms.pdf) - [Graph Intention Network for Click-through Rate Prediction in Sponsored Search](Match/GNN/Graph%20Intention%20Network%20for%20Click-through%20Rate%20Prediction%20in%20Sponsored%20Search.pdf) - [Graph Neural Network for Tag Ranking in Tag-enhanced Video Recommendation](Match/GNN/Graph%20Neural%20Network%20for%20Tag%20Ranking%20in%20Tag-enhanced%20Video%20Recommendation.pdf) - [Graph Neural Networks for Social Recommendation](Match/GNN/Graph%20Neural%20Networks%20for%20Social%20Recommendation.pdf) - [GraphSAIL - Graph Structure Aware Incremental Learning for Recommender Systems](Match/GNN/GraphSAIL%20-%20Graph%20Structure%20Aware%20Incremental%20Learning%20for%20Recommender%20Systems.pdf) - [Hessian-aware Quantized Node Embeddings for Recommendation](Match/GNN/Hessian-aware%20Quantized%20Node%20Embeddings%20for%20Recommendation.pdf) - [Improving Accuracy and Diversity in Matching of Recommendation with Diversified Preference Network](Match/GNN/Improving%20Accuracy%20and%20Diversity%20in%20Matching%20of%20Recommendation%20with%20Diversified%20Preference%20Network.pdf) - [IntentGC - a Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation](Match/GNN/IntentGC%20-%20a%20Scalable%20Graph%20Convolution%20Framework%20Fusing%20Heterogeneous%20Information%20for%20Recommendation.pdf) - [LightSAGE - Graph Neural Networks for Large Scale Item Retrieval in Shopee’s Advertisement Recommendation](Match/GNN/LightSAGE%20-%20Graph%20Neural%20Networks%20for%20Large%20Scale%20Item%20Retrieval%20in%20Shopee%E2%80%99s%20Advertisement%20Recommendation.pdf) - [MultiSage - Empowering GCN with Contextualized Multi-Embeddings on Web-Scale Multipartite Networks](Match/GNN/MultiSage%20-%20Empowering%20GCN%20with%20Contextualized%20Multi-Embeddings%20on%20Web-Scale%20Multipartite%20Networks.pdf) - [Modeling Dual Period-Varying Preferences for Takeaway Recommendation](Match/GNN/Modeling%20Dual%20Period-Varying%20Preferences%20for%20Takeaway%20Recommendation.pdf) - [MMGCN - Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video](Match/GNN/MMGCN%20-%20Multi-modal%20Graph%20Convolution%20Network%20for%20Personalized%20Recommendation%20of%20Micro-video.pdf) - [MultiBiSage - A Web-Scale Recommendation System Using Multiple Bipartite Graphs at Pinterest](Match/GNN/MultiBiSage%20-%20A%20Web-Scale%20Recommendation%20System%20Using%20Multiple%20Bipartite%20Graphs%20at%20Pinterest.pdf) - [M2GRL - A Multi-task Multi-view Graph Representation Learning Framework for Web-scale Recommender Systems](Match/GNN/M2GRL%20-%20A%20Multi-task%20Multi-view%20Graph%20Representation%20Learning%20Framework%20for%20Web-scale%20Recommender%20Systems.pdf) - [Network Embedding as Matrix Factorization - Unifying DeepWalk, LINE, PTE, and node2vec](Match/GNN/Network%20Embedding%20as%20Matrix%20Factorization%20-%20Unifying%20DeepWalk%2C%20LINE%2C%20PTE%2C%20and%20node2vec.pdf) - [Neighbor Interaction Aware Graph Convolution Networks for Recommendation](Match/GNN/Neighbor%20Interaction%20Aware%20Graph%20Convolution%20Networks%20for%20Recommendation.pdf) - [OmniSage - Large Scale, Multi-Entity Heterogeneous Graph Representation Learning](Match/GNN/OmniSage%20-%20Large%20Scale%2C%20Multi-Entity%20Heterogeneous%20Graph%20Representation%20Learning.pdf) - [Package Recommendation with Intra- and Inter-Package Attention Networks](Match/GNN/Package%20Recommendation%20with%20Intra-%20and%20Inter-Package%20Attention%20Networks.pdf) - [ProNE - Fast and Scalable Network Representation Learning](Match/GNN/ProNE%20-%20Fast%20and%20Scalable%20Network%20Representation%20Learning.pdf) - [Representation Learning for Attributed Multiplex Heterogeneous Network](Match/GNN/Representation%20Learning%20for%20Attributed%20Multiplex%20Heterogeneous%20Network.pdf) - [Revisiting Item Promotion in GNN-based Collaborative Filtering - A Masked Targeted Topological Attack Perspective](Match/GNN/Revisiting%20Item%20Promotion%20in%20GNN-based%20Collaborative%20Filtering%20-%20A%20Masked%20Targeted%20Topological%20Attack%20Perspective.pdf) - [Self-supervised Graph Learning for Recommendation](Match/GNN/Self-supervised%20Graph%20Learning%20for%20Recommendation.pdf) - [SVD-GCN - A Simplified Graph Convolution Paradigm for Recommendation](Match/GNN/SVD-GCN%20-%20A%20Simplified%20Graph%20Convolution%20Paradigm%20for%20Recommendation.pdf) - [Spherical Graph Embedding for Item Retrieval in Recommendation System](Match/GNN/Spherical%20Graph%20Embedding%20for%20Item%20Retrieval%20in%20Recommendation%20System.pdf) - [SimClusters - Community-Based Representations for Heterogeneous Recommendations at Twitter](Match/GNN/SimClusters%20-%20Community-Based%20Representations%20for%20Heterogeneous%20Recommendations%20at%20Twitter.pdf) - [Self-Supervised Hypergraph Transformer for Recommender Systems](Match/GNN/Self-Supervised%20Hypergraph%20Transformer%20for%20Recommender%20Systems.pdf) - [TwHIN - Embedding the Twitter Heterogeneous Information Network for Personalized Recommendation](Match/GNN/TwHIN%20-%20Embedding%20the%20Twitter%20Heterogeneous%20Information%20Network%20for%20Personalized%20Recommendation.pdf) - [Zero-shot Item-based Recommendation via Multi-task Product Knowledge Graph Pre-Training](Match/GNN/Zero-shot%20Item-based%20Recommendation%20via%20Multi-task%20Product%20Knowledge%20Graph%20Pre-Training.pdf) - [metapath2vec - Scalable Representation Learning for Heterogeneous Networks](Match/GNN/metapath2vec%20-%20Scalable%20Representation%20Learning%20for%20Heterogeneous%20Networks.pdf) - [struc2vec - Learning Node Representations from Structural Identity](Match/GNN/struc2vec%20-%20Learning%20Node%20Representations%20from%20Structural%20Identity.pdf) ## Multi-Task - [[2012][MGDA] Multiple-gradient descent algorithm (MGDA) for multiobjective optimization](Multi-Task/%5B2012%5D%5BMGDA%5D%20Multiple-gradient%20descent%20algorithm%20%28MGDA%29%20for%20multiobjective%20optimization.pdf) - [[2018][Alibaba][ESMM] Entire Space Multi-Task Model - An Effective Approach for Estimating Post-Click Conversion Rate](Multi-Task/%5B2018%5D%5BAlibaba%5D%5BESMM%5D%20Entire%20Space%20Multi-Task%20Model%20-%20An%20Effective%20Approach%20for%20Estimating%20Post-Click%20Conversion%20Rate.pdf) - [[2018][MagicLeap][GradNorm] GradNorm - Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks](Multi-Task/%5B2018%5D%5BMagicLeap%5D%5BGradNorm%5D%20GradNorm%20-%20Gradient%20Normalization%20for%20Adaptive%20Loss%20Balancing%20in%20Deep%20Multitask%20Networks.pdf) - [[2018][Google][MMOE] Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts](Multi-Task/%5B2018%5D%5BGoogle%5D%5BMMOE%5D%20Modeling%20Task%20Relationships%20in%20Multi-task%20Learning%20with%20Multi-gate%20Mixture-of-Experts.pdf) - [[2018][Cambridge] Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](Multi-Task/%5B2018%5D%5BCambridge%5D%20Multi-Task%20Learning%20Using%20Uncertainty%20to%20Weigh%20Losses%20for%20Scene%20Geometry%20and%20Semantics.pdf) - [[2019][Alibaba][DBMTL] Deep Bayesian Multi-Target Learning for Recommender Systems](Multi-Task/%5B2019%5D%5BAlibaba%5D%5BDBMTL%5D%20Deep%20Bayesian%20Multi-Target%20Learning%20for%20Recommender%20Systems.pdf) - [[2019][Intel] Multi-Task Learning as Multi-Objective Optimization](Multi-Task/%5B2019%5D%5BIntel%5D%20Multi-Task%20Learning%20as%20Multi-Objective%20Optimization.pdf) - [[2019][Youtube] Recommending What Video to Watch Next - A Multitask Ranking System](Multi-Task/%5B2019%5D%5BYoutube%5D%20Recommending%20What%20Video%20to%20Watch%20Next%20-%20A%20Multitask%20Ranking%20System.pdf) - [[2019][Alibaba] A Pareto-Efficient Algorithm for Multiple Objective Optimization in E-Commerce Recommendation](Multi-Task/%5B2019%5D%5BAlibaba%5D%20A%20Pareto-Efficient%20Algorithm%20for%20Multiple%20Objective%20Optimization%20in%20E-Commerce%20Recommendation.pdf) - [[2020][Alibaba][Multi-IPW&Multi-DR] LARGE-SCALE CAUSAL APPROACHES TO DEBIASING POST-CLICK CONVERSION RATE ESTIMATION WITH MULTI-TASK LEARNING](Multi-Task/%5B2020%5D%5BAlibaba%5D%5BMulti-IPW%26Multi-DR%5D%20LARGE-SCALE%20CAUSAL%20APPROACHES%20TO%20DEBIASING%20POST-CLICK%20CONVERSION%20RATE%20ESTIMATION%20WITH%20MULTI-TASK%20LEARNING.pdf) - [[2020][Tencent][PLE] Progressive Layered Extraction (PLE) - A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations](Multi-Task/%5B2020%5D%5BTencent%5D%5BPLE%5D%20Progressive%20Layered%20Extraction%20%28PLE%29%20-%20A%20Novel%20Multi-Task%20Learning%20%28MTL%29%20Model%20for%20Personalized%20Recommendations.pdf) - [[2020][Google][MoSE] Multitask Mixture of Sequential Experts for User Activity Streams](Multi-Task/%5B2020%5D%5BGoogle%5D%5BMoSE%5D%20Multitask%20Mixture%20of%20Sequential%20Experts%20for%20User%20Activity%20Streams.pdf) - [[2020][JD][DMT] Deep Multifaceted Transformers for Multi-objective Ranking in Large-Scale E-commerce Recommender Systems](Multi-Task/%5B2020%5D%5BJD%5D%5BDMT%5D%20Deep%20Multifaceted%20Transformers%20for%20Multi-objective%20Ranking%20in%20Large-Scale%20E-commerce%20Recommender%20Systems.pdf) - [[2020][PCGrad] Gradient Surgery for Multi-Task Learning](Multi-Task/%5B2020%5D%5BPCGrad%5D%20Gradient%20Surgery%20for%20Multi-Task%20Learning.pdf) - [[2021][Meituan][AITM] Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising](Multi-Task/%5B2021%5D%5BMeituan%5D%5BAITM%5D%20Modeling%20the%20Sequential%20Dependence%20among%20Audience%20Multi-step%20Conversions%20with%20Multi-task%20Learning%20in%20Targeted%20Display%20Advertising.pdf) - [[2022][Alibaba][ESCM2] ESCM2 - Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation](Multi-Task/%5B2022%5D%5BAlibaba%5D%5BESCM2%5D%20ESCM2%20-%20Entire%20Space%20Counterfactual%20Multi-Task%20Model%20for%20Post-Click%20Conversion%20Rate%20Estimation.pdf) - [A CLOSER LOOK AT LOSS WEIGHTING IN MULTI-TASK LEARNING](Multi-Task/A%20CLOSER%20LOOK%20AT%20LOSS%20WEIGHTING%20IN%20MULTI-TASK%20LEARNING.pdf) - [AdaTask - A Task-aware Adaptive Learning Rate Approach to Multi-task Learning](Multi-Task/AdaTask%20-%20A%20Task-aware%20Adaptive%20Learning%20Rate%20Approach%20to%20Multi-task%20Learning.pdf) - [AdaTT - Adaptive Task-to-Task Fusion Network for Multitask Learning in Recommendations](Multi-Task/AdaTT%20-%20Adaptive%20Task-to-Task%20Fusion%20Network%20for%20Multitask%20Learning%20in%20Recommendations.pdf) - [Can Small Heads Help Understanding and Improving Multi-Task Generalization](Multi-Task/Can%20Small%20Heads%20Help%20Understanding%20and%20Improving%20Multi-Task%20Generalization.pdf) - [Cross-stitch Networks for Multi-task Learning](Multi-Task/Cross-stitch%20Networks%20for%20Multi-task%20Learning.pdf) - [Conflict-Averse Gradient Descent for Multi-task Learning](Multi-Task/Conflict-Averse%20Gradient%20Descent%20for%20Multi-task%20Learning.pdf) - [CAM2 - Conformity-Aware Multi-Task Ranking Model for Large-Scale Recommender Systems](Multi-Task/CAM2%20-%20Conformity-Aware%20Multi-Task%20Ranking%20Model%20for%20Large-Scale%20Recommender%20Systems.pdf) - [Dynamic Task Prioritization for Multitask Learning](Multi-Task/Dynamic%20Task%20Prioritization%20for%20Multitask%20Learning.pdf) - [Deep Mutual Learning across Task Towers for Effective Multi-Task Recommender Learning](Multi-Task/Deep%20Mutual%20Learning%20across%20Task%20Towers%20for%20Effective%20Multi-Task%20Recommender%20Learning.pdf) - [Deep Task-specific Bottom Representation Network for Multi-Task Recommendation](Multi-Task/Deep%20Task-specific%20Bottom%20Representation%20Network%20for%20Multi-Task%20Recommendation.pdf) - [DTN - Deep Multiple Task-specific Feature Interactions Network for Multi-Task Recommendation](Multi-Task/DTN%20-%20Deep%20Multiple%20Task-specific%20Feature%20Interactions%20Network%20for%20Multi-Task%20Recommendation.pdf) - [DSelect-k - Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning](Multi-Task/DSelect-k%20-%20Differentiable%20Selection%20in%20the%20Mixture%20of%20Experts%20with%20Applications%20to%20Multi-Task%20Learning.pdf) - [Entire Space Multi-Task Modeling via Post-Click Behavior Decomposition for Conversion Rate Prediction](Multi-Task/Entire%20Space%20Multi-Task%20Modeling%20via%20Post-Click%20Behavior%20Decomposition%20for%20Conversion%20Rate%20Prediction.pdf) - [Feature Decomposition for Reducing Negative Transfer - A Novel Multi-task Learning Method for Recommender System](Multi-Task/Feature%20Decomposition%20for%20Reducing%20Negative%20Transfer%20-%20A%20Novel%20Multi-task%20Learning%20Method%20for%20Recommender%20System.pdf) - [Hierarchically Modeling Micro and Macro Behaviors via Multi-Task Learning for Conversion Rate Prediction](Multi-Task/Hierarchically%20Modeling%20Micro%20and%20Macro%20Behaviors%20via%20Multi-Task%20Learning%20for%20Conversion%20Rate%20Prediction.pdf) - [HoME - Hierarchy of Multi-Gate Experts for Multi-Task Learning at Kuaishou](Multi-Task/HoME%20-%20Hierarchy%20of%20Multi-Gate%20Experts%20for%20Multi-Task%20Learning%20at%20Kuaishou.pdf) - [HarmonRank - Ranking-aligned Multi-objective Ensemble for Live-streaming E-commerce Recommendation](Multi-Task/HarmonRank%20-%20Ranking-aligned%20Multi-objective%20Ensemble%20for%20Live-streaming%20E-commerce%20Recommendation.pdf) - [HyperGrid Transformers - Towards A Single Model for Multiple Tasks](Multi-Task/HyperGrid%20Transformers%20-%20Towards%20A%20Single%20Model%20for%20Multiple%20Tasks.pdf) - [Improving Training Stability for Multitask Ranking Models in Recommender Systems](Multi-Task/Improving%20Training%20Stability%20for%20Multitask%20Ranking%20Models%20in%20Recommender%20Systems.pdf) - [INFNet - A Task-aware Information Flow Network for Large-Scale Recommendation Systems](Multi-Task/INFNet%20-%20A%20Task-aware%20Information%20Flow%20Network%20for%20Large-Scale%20Recommendation%20Systems.pdf) - [Learning to Recommend with Multiple Cascading Behaviors](Multi-Task/Learning%20to%20Recommend%20with%20Multiple%20Cascading%20Behaviors.pdf) - [MetaBalance - Improving Multi-Task Recommendations via Adapting Gradient Magnitudes of Auxiliary Tasks](Multi-Task/MetaBalance%20-%20Improving%20Multi-Task%20Recommendations%20via%20Adapting%20Gradient%20Magnitudes%20of%20Auxiliary%20Tasks.pdf) - [MSSM - A Multiple-level Sparse Sharing Model for Efficient Multi-Task Learning](Multi-Task/MSSM%20-%20A%20Multiple-level%20Sparse%20Sharing%20Model%20for%20Efficient%20Multi-Task%20Learning.pdf) - [Multi-objective Learning to Rank by Model Distillation](Multi-Task/Multi-objective%20Learning%20to%20Rank%20by%20Model%20Distillation.pdf) - [MultiBalance - Multi-Objective Gradient Balancing in Industrial-Scale Multi-Task Recommendation System](Multi-Task/MultiBalance%20-%20Multi-Objective%20Gradient%20Balancing%20in%20Industrial-Scale%20Multi-Task%20Recommendation%20System.pdf) - [Mixture of Virtual-Kernel Experts for Multi-Objective User Profile Modeling](Multi-Task/Mixture%20of%20Virtual-Kernel%20Experts%20for%20Multi-Objective%20User%20Profile%20Modeling.pdf) - [Multitask Ranking System for Immersive Feed and No More Clicks - A Case Study of Short-Form Video Recommendation](Multi-Task/Multitask%20Ranking%20System%20for%20Immersive%20Feed%20and%20No%20More%20Clicks%20-%20A%20Case%20Study%20of%20Short-Form%20Video%20Recommendation.pdf) - [Multi-Objective Ranking Optimization for Product Search Using Stochastic Label Aggregation](Multi-Task/Multi-Objective%20Ranking%20Optimization%20for%20Product%20Search%20Using%20Stochastic%20Label%20Aggregation.pdf) - [Multi-Task Learning for Dense Prediction Tasks - A Survey](Multi-Task/Multi-Task%20Learning%20for%20Dense%20Prediction%20Tasks%20-%20A%20Survey.pdf) - [Multi-Task Learning as Multi-Objective Optimization - slide](Multi-Task/Multi-Task%20Learning%20as%20Multi-Objective%20Optimization%20-%20slide.pdf) - [Multi-Task Deep Recommender Systems - A Survey](Multi-Task/Multi-Task%20Deep%20Recommender%20Systems%20-%20A%20Survey.pdf) - [NCS4CVR - Neuron-Connection Sharing for Multi-Task Learning in Video Conversion Rate Prediction](Multi-Task/NCS4CVR%20-%20Neuron-Connection%20Sharing%20for%20Multi-Task%20Learning%20in%20Video%20Conversion%20Rate%20Prediction.pdf) - [No More Tuning - Prioritized Multi-Task Learning with Lagrangian Differential Multiplier Methods](Multi-Task/No%20More%20Tuning%20-%20Prioritized%20Multi-Task%20Learning%20with%20Lagrangian%20Differential%20Multiplier%20Methods.pdf) - [Optimizing Airbnb Search Journey with Multi-task Learning](Multi-Task/Optimizing%20Airbnb%20Search%20Journey%20with%20Multi-task%20Learning.pdf) - [Perceive Your Users in Depth - Learning Universal User Representations from Multiple E-commerce Tasks](Multi-Task/Perceive%20Your%20Users%20in%20Depth%20-%20Learning%20Universal%20User%20Representations%20from%20Multiple%20E-commerce%20Tasks.pdf) - [Pantheon - Personalized Multi-objective Ensemble Sort via Iterative Pareto Policy Optimization](Multi-Task/Pantheon%20-%20Personalized%20Multi-objective%20Ensemble%20Sort%20via%20Iterative%20Pareto%20Policy%20Optimization.pdf) - [Personalized Approximate Pareto-Efficient Recommendation](Multi-Task/Personalized%20Approximate%20Pareto-Efficient%20Recommendation.pdf) - [Pareto Multi-Task Learning](Multi-Task/Pareto%20Multi-Task%20Learning.pdf) - [Single-shot Feature Selection for Multi-task Recommendations](Multi-Task/Single-shot%20Feature%20Selection%20for%20Multi-task%20Recommendations.pdf) - [STEM - Unleashing the Power of Embeddings for Multi-task Recommendation](Multi-Task/STEM%20-%20Unleashing%20the%20Power%20of%20Embeddings%20for%20Multi-task%20Recommendation.pdf) - [STAN - Stage-Adaptive Network for Multi-Task Recommendation by Learning User Lifecycle-Based Representation](Multi-Task/STAN%20-%20Stage-Adaptive%20Network%20for%20Multi-Task%20Recommendation%20by%20Learning%20User%20Lifecycle-Based%20Representation.pdf) - [SNR - Sub-Network Routing for Flexible Parameter Sharing in Multi-Task Learning](Multi-Task/SNR%20-%20Sub-Network%20Routing%20for%20Flexible%20Parameter%20Sharing%20in%20Multi-Task%20Learning.pdf) - [Touch the Core - Exploring Task Dependence Among Hybrid Targets for Recommendation](Multi-Task/Touch%20the%20Core%20-%20Exploring%20Task%20Dependence%20Among%20Hybrid%20Targets%20for%20Recommendation.pdf) - [Utilizing Non-click Samples via Semi-supervised Learning for Conversion Rate Prediction](Multi-Task/Utilizing%20Non-click%20Samples%20via%20Semi-supervised%20Learning%20for%20Conversion%20Rate%20Prediction.pdf) - [Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning](Multi-Task/Understanding%20and%20Improving%20Fairness-Accuracy%20Trade-offs%20in%20Multi-Task%20Learning.pdf) - [Why I like it - multi-task learning for recommendation and explanation](Multi-Task/Why%20I%20like%20it%20-%20multi-task%20learning%20for%20recommendation%20and%20explanation.pdf) - [xMTF - A Formula-Free Model for Reinforcement-Learning-Based Multi-Task Fusion in Recommender Systems](Multi-Task/xMTF%20-%20A%20Formula-Free%20Model%20for%20Reinforcement-Learning-Based%20Multi-Task%20Fusion%20in%20Recommender%20Systems.pdf) ## Multi-Modal - [Adversarial Multimodal Representation Learning for Click-Through Rate Prediction](Multi-Modal/Adversarial%20Multimodal%20Representation%20Learning%20for%20Click-Through%20Rate%20Prediction.pdf) - [Adaptive Multi-Modalities Fusion in Sequential Recommendation Systems](Multi-Modal/Adaptive%20Multi-Modalities%20Fusion%20in%20Sequential%20Recommendation%20Systems.pdf) - [Bootstrap Latent Representations for Multi-modal Recommendation](Multi-Modal/Bootstrap%20Latent%20Representations%20for%20Multi-modal%20Recommendation.pdf) - [ContentCTR - Frame-level Live Streaming Click-Through Rate Prediction with Multimodal Transformer](Multi-Modal/ContentCTR%20-%20Frame-level%20Live%20Streaming%20Click-Through%20Rate%20Prediction%20with%20Multimodal%20Transformer.pdf) - [COURIER - Contrastive User Intention Reconstruction for Large-Scale Pre-Train of Image Features](Multi-Modal/COURIER%20-%20Contrastive%20User%20Intention%20Reconstruction%20for%20Large-Scale%20Pre-Train%20of%20Image%20Features.pdf) - [End-to-end training of Multimodal Model and ranking Model](Multi-Modal/End-to-end%20training%20of%20Multimodal%20Model%20and%20ranking%20Model.pdf) - [Heterogeneous Attention Network for Effective and Efficient Cross-modal Retrieval](Multi-Modal/Heterogeneous%20Attention%20Network%20for%20Effective%20and%20Efficient%20Cross-modal%20Retrieval.pdf) - [Learning Joint Embedding with Multimodal Cues for Cross-Modal Video-Text Retrieva](Multi-Modal/Learning%20Joint%20Embedding%20with%20Multimodal%20Cues%20for%20Cross-Modal%20Video-Text%20Retrieva.pdf) - [Multimodal Recommender Systems - A Survey](Multi-Modal/Multimodal%20Recommender%20Systems%20-%20A%20Survey.pdf) - [Multi-Modal Self-Supervised Learning for Recommendation](Multi-Modal/Multi-Modal%20Self-Supervised%20Learning%20for%20Recommendation.pdf) - [MM-GEF - Multi-modal representation meet collaborative filtering](Multi-Modal/MM-GEF%20-%20Multi-modal%20representation%20meet%20collaborative%20filtering.pdf) - [MMBee - Live Streaming Gift-Sending Recommendations via Multi-Modal Fusion and Behaviour Expansion](Multi-Modal/MMBee%20-%20Live%20Streaming%20Gift-Sending%20Recommendations%20via%20Multi-Modal%20Fusion%20and%20Behaviour%20Expansion.pdf) - [Pretraining Representations of Multi-modal Multi-query E-commerce Search](Multi-Modal/Pretraining%20Representations%20of%20Multi-modal%20Multi-query%20E-commerce%20Search.pdf) - [QARM - Quantitative Alignment Multi-Modal Recommendation at Kuaishou](Multi-Modal/QARM%20-%20Quantitative%20Alignment%20Multi-Modal%20Recommendation%20at%20Kuaishou.pdf) - [Universal Multi-modal Multi-domain Pre-trained Recommendation](Multi-Modal/Universal%20Multi-modal%20Multi-domain%20Pre-trained%20Recommendation.pdf) - [Unsupervised Multi-Modal Representation Learning for High Quality Retrieval of Similar Products at E-commerce Scale](Multi-Modal/Unsupervised%20Multi-Modal%20Representation%20Learning%20for%20High%20Quality%20Retrieval%20of%20Similar%20Products%20at%20E-commerce%20Scale.pdf) ## Multi-Scenario - [[2020][JD][DADNN] DADNN - Multi-Scene CTR Prediction via Domain-Aware Deep Neural Network](Multi-Scenario/%5B2020%5D%5BJD%5D%5BDADNN%5D%20DADNN%20-%20Multi-Scene%20CTR%20Prediction%20via%20Domain-Aware%20Deep%20Neural%20Network.pdf) - [[2021][Alibaba][STAR] One Model to Serve All - Star Topology Adaptive Recommenderfor Multi-Domain CTR Prediction](Multi-Scenario/%5B2021%5D%5BAlibaba%5D%5BSTAR%5D%20One%20Model%20to%20Serve%20All%20-%20Star%20Topology%20Adaptive%20Recommenderfor%20Multi-Domain%20CTR%20Prediction.pdf) - [[2021][Baidu] Multi-Task and Multi-Scene Unified Ranking Model for Online Advertising](Multi-Scenario/%5B2021%5D%5BBaidu%5D%20Multi-Task%20and%20Multi-Scene%20Unified%20Ranking%20Model%20for%20Online%20Advertising.pdf) - [[2022][AntGroup][AESM2] Automatic Expert Selection for Multi-Scenario and Multi-Task Search](Multi-Scenario/%5B2022%5D%5BAntGroup%5D%5BAESM2%5D%20Automatic%20Expert%20Selection%20for%20Multi-Scenario%20and%20Multi-Task%20Search.pdf) - [[2023][Meituan][HiNet] HiNet - Novel Multi-Scenario & Multi-Task Learning with Hierarchical Information Extraction](Multi-Scenario/%5B2023%5D%5BMeituan%5D%5BHiNet%5D%20HiNet%20-%20Novel%20Multi-Scenario%20%26%20Multi-Task%20Learning%20with%20Hierarchical%20Information%20Extraction.pdf) - [[2023][Kuaishou][PEPNet] PEPNet - Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information](Multi-Scenario/%5B2023%5D%5BKuaishou%5D%5BPEPNet%5D%20PEPNet%20-%20Parameter%20and%20Embedding%20Personalized%20Network%20for%20Infusing%20with%20Personalized%20Prior%20Information.pdf) - [A Deep Framework for Cross-Domain and Cross-System Recommendations](Multi-Scenario/A%20Deep%20Framework%20for%20Cross-Domain%20and%20Cross-System%20Recommendations.pdf) - [APG - Adaptive Parameter Generation Network for Click-Through Rate Prediction](Multi-Scenario/APG%20-%20Adaptive%20Parameter%20Generation%20Network%20for%20Click-Through%20Rate%20Prediction.pdf) - [ADL - Adaptive Distribution Learning Framework for Multi-Scenario CTR Prediction](Multi-Scenario/ADL%20-%20Adaptive%20Distribution%20Learning%20Framework%20for%20Multi-Scenario%20CTR%20Prediction.pdf) - [AdaSparse - Learning Adaptively Sparse Structures for Multi-Domain Click-Through Rate Prediction](Multi-Scenario/AdaSparse%20-%20Learning%20Adaptively%20Sparse%20Structures%20for%20Multi-Domain%20Click-Through%20Rate%20Prediction.pdf) - [A Collaborative Transfer Learning Framework for Cross-domain Recommendation](Multi-Scenario/A%20Collaborative%20Transfer%20Learning%20Framework%20for%20Cross-domain%20Recommendation.pdf) - [A Survey on Cross-domain Recommendation - Taxonomies, Methods, and Future Directions](Multi-Scenario/A%20Survey%20on%20Cross-domain%20Recommendation%20-%20Taxonomies%2C%20Methods%2C%20and%20Future%20Directions.pdf) - [Adaptive Conditional Expert Selection Network for Multi-domain Recommendation](Multi-Scenario/Adaptive%20Conditional%20Expert%20Selection%20Network%20for%20Multi-domain%20Recommendation.pdf) - [Adaptive Domain Interest Network for Multi-domain Recommendation](Multi-Scenario/Adaptive%20Domain%20Interest%20Network%20for%20Multi-domain%20Recommendation.pdf) - [A Unified Search and Recommendation Framework Based on Multi-Scenario Learning for Ranking in E-commerce](Multi-Scenario/A%20Unified%20Search%20and%20Recommendation%20Framework%20Based%20on%20Multi-Scenario%20Learning%20for%20Ranking%20in%20E-commerce.pdf) - [BOMGraph - Boosting Multi-scenario E-commerce Search with a Unified Graph Neural Network](Multi-Scenario/BOMGraph%20-%20Boosting%20Multi-scenario%20E-commerce%20Search%20with%20a%20Unified%20Graph%20Neural%20Network.pdf) - [Cross-domain recommendation via user interest alignment](Multi-Scenario/Cross-domain%20recommendation%20via%20user%20interest%20alignment.pdf) - [Cross-Domain Recommendation- Challenges, Progress, and Prospects](Multi-Scenario/Cross-Domain%20Recommendation-%20Challenges%2C%20Progress%2C%20and%20Prospects.pdf) - [Cross-domain Augmentation Networks for Click-Through Rate Prediction](Multi-Scenario/Cross-domain%20Augmentation%20Networks%20for%20Click-Through%20Rate%20Prediction.pdf) - [Cross Domain Recommendation via Bi-directional Transfer Graph Collaborative Filtering Networks](Multi-Scenario/Cross%20Domain%20Recommendation%20via%20Bi-directional%20Transfer%20Graph%20Collaborative%20Filtering%20Networks.pdf) - [CDR-Adapter - Learning Adapters to Dig Out More Transferring Ability for Cross-Domain Recommendation Models](Multi-Scenario/CDR-Adapter%20-%20Learning%20Adapters%20to%20Dig%20Out%20More%20Transferring%20Ability%20for%20Cross-Domain%20Recommendation%20Models.pdf) - [Cross-Domain Recommendation - An Embedding and Mapping Approach](Multi-Scenario/Cross-Domain%20Recommendation%20-%20An%20Embedding%20and%20Mapping%20Approach.pdf) - [CoNet - Collaborative Cross Networks for Cross-Domain Recommendation](Multi-Scenario/CoNet%20-%20Collaborative%20Cross%20Networks%20for%20Cross-Domain%20Recommendation.pdf) - [Cross domain recommendation based on multi-type media fusion](Multi-Scenario/Cross%20domain%20recommendation%20based%20on%20multi-type%20media%20fusion.pdf) - [Cross-domain Recommendation Without Sharing User-relevant Data](Multi-Scenario/Cross-domain%20Recommendation%20Without%20Sharing%20User-relevant%20Data.pdf) - [Correlative Preference Transfer with Hierarchical Hypergraph Network for Multi-Domain Recommendation](Multi-Scenario/Correlative%20Preference%20Transfer%20with%20Hierarchical%20Hypergraph%20Network%20for%20Multi-Domain%20Recommendation.pdf) - [Cross-Domain Recommendation to Cold-Start Users via Variational Information Bottleneck](Multi-Scenario/Cross-Domain%20Recommendation%20to%20Cold-Start%20Users%20via%20Variational%20Information%20Bottleneck.pdf) - [Continual Transfer Learning for Cross-Domain Click-Through Rate Prediction at Taobao](Multi-Scenario/Continual%20Transfer%20Learning%20for%20Cross-Domain%20Click-Through%20Rate%20Prediction%20at%20Taobao.pdf) - [Cross-Domain Sequential Recommendation via Neural Process](Multi-Scenario/Cross-Domain%20Sequential%20Recommendation%20via%20Neural%20Process.pdf) - [DTCDR - A Framework for Dual-Target Cross-Domain Recommendation](Multi-Scenario/DTCDR%20-%20A%20Framework%20for%20Dual-Target%20Cross-Domain%20Recommendation.pdf) - [Dynamic collaborative filtering Thompson Sampling for cross-domain advertisements recommendation](Multi-Scenario/Dynamic%20collaborative%20filtering%20Thompson%20Sampling%20for%20cross-domain%20advertisements%20recommendation.pdf) - [DDTCDR - Deep Dual Transfer Cross Domain Recommendation](Multi-Scenario/DDTCDR%20-%20Deep%20Dual%20Transfer%20Cross%20Domain%20Recommendation.pdf) - [DeepAPF - Deep Attentive Probabilistic Factorization for Multi-site Video Recommendation](Multi-Scenario/DeepAPF%20-%20Deep%20Attentive%20Probabilistic%20Factorization%20for%20Multi-site%20Video%20Recommendation.pdf) - [DisenCDR - Learning Disentangled Representations for Cross-Domain Recommendation](Multi-Scenario/DisenCDR%20-%20Learning%20Disentangled%20Representations%20for%20Cross-Domain%20Recommendation.pdf) - [DIIT - A Domain-Invariant Information Transfer Method for Industrial Cross-Domain Recommendation](Multi-Scenario/DIIT%20-%20A%20Domain-Invariant%20Information%20Transfer%20Method%20for%20Industrial%20Cross-Domain%20Recommendation.pdf) - [EXIT - An EXplicit Interest Transfer Framework for Cross-Domain Recommendation](Multi-Scenario/EXIT%20-%20An%20EXplicit%20Interest%20Transfer%20Framework%20for%20Cross-Domain%20Recommendation.pdf) - [Heterogeneous Graph Augmented Multi-Scenario Sharing Recommendation with Tree-Guided Expert Networks](Multi-Scenario/Heterogeneous%20Graph%20Augmented%20Multi-Scenario%20Sharing%20Recommendation%20with%20Tree-Guided%20Expert%20Networks.pdf) - [Hybrid Contrastive Constraints for Multi-Scenario Ad Ranking](Multi-Scenario/Hybrid%20Contrastive%20Constraints%20for%20Multi-Scenario%20Ad%20Ranking.pdf) - [HAMUR - Hyper Adapter for Multi-Domain Recommendation](Multi-Scenario/HAMUR%20-%20Hyper%20Adapter%20for%20Multi-Domain%20Recommendation.pdf) - [IncMSR - An Incremental Learning Approach for Multi-Scenario Recommendation](Multi-Scenario/IncMSR%20-%20An%20Incremental%20Learning%20Approach%20for%20Multi-Scenario%20Recommendation.pdf) - [Improving Multi-Scenario Learning to Rank in E-commerce by Exploiting Task Relationships in the Label Space](Multi-Scenario/Improving%20Multi-Scenario%20Learning%20to%20Rank%20in%20E-commerce%20by%20Exploiting%20Task%20Relationships%20in%20the%20Label%20Space.pdf) - [KEEP - An Industrial Pre-Training Framework for Online Recommendation via Knowledge Extraction and Plugging](Multi-Scenario/KEEP%20-%20An%20Industrial%20Pre-Training%20Framework%20for%20Online%20Recommendation%20via%20Knowledge%20Extraction%20and%20Plugging.pdf) - [Leaving No One Behind - A Multi-Scenario Multi-Task Meta Learning Approach for Advertiser Modeling](Multi-Scenario/Leaving%20No%20One%20Behind%20-%20A%20Multi-Scenario%20Multi-Task%20Meta%20Learning%20Approach%20for%20Advertiser%20Modeling.pdf) - [M3oE - Multi-Domain Multi-Task Mixture-of-Experts Recommendation Framework](Multi-Scenario/M3oE%20-%20Multi-Domain%20Multi-Task%20Mixture-of-Experts%20Recommendation%20Framework.pdf) - [Moment&Cross - Next-Generation Real-Time Cross-Domain CTR Prediction for Live-Streaming Recommendation at Kuaishou](Multi-Scenario/Moment%26Cross%20-%20Next-Generation%20Real-Time%20Cross-Domain%20CTR%20Prediction%20for%20Live-Streaming%20Recommendation%20at%20Kuaishou.pdf) - [Mixed Attention Network for Cross-domain Sequential Recommendation](Multi-Scenario/Mixed%20Attention%20Network%20for%20Cross-domain%20Sequential%20Recommendation.pdf) - [Multi-Graph based Multi-Scenario Recommendation in Large-scale Online Video Services](Multi-Scenario/Multi-Graph%20based%20Multi-Scenario%20Recommendation%20in%20Large-scale%20Online%20Video%20Services.pdf) - [M-scan - A Multi-Scenario Causal-driven Adaptive Network for Recommendation](Multi-Scenario/M-scan%20-%20A%20Multi-Scenario%20Causal-driven%20Adaptive%20Network%20for%20Recommendation.pdf) - [MLoRA - Multi-Domain Low-Rank Adaptive Network for Click-Through Rate Prediction](Multi-Scenario/MLoRA%20-%20Multi-Domain%20Low-Rank%20Adaptive%20Network%20for%20Click-Through%20Rate%20Prediction.pdf) - [Multi-Scenario Ranking with Adaptive Feature Learning](Multi-Scenario/Multi-Scenario%20Ranking%20with%20Adaptive%20Feature%20Learning.pdf) - [MTMD - A Multi-Task Multi-Domain Framework for Unified Ad Lightweight Ranking at Pinterestpdf](Multi-Scenario/MTMD%20-%20A%20Multi-Task%20Multi-Domain%20Framework%20for%20Unified%20Ad%20Lightweight%20Ranking%20at%20Pinterestpdf.pdf) - [Personalized Transfer of User Preferences for Cross-domain Recommendation](Multi-Scenario/Personalized%20Transfer%20of%20User%20Preferences%20for%20Cross-domain%20Recommendation.pdf) - [Rethinking Cross-Domain Sequential Recommendation under Open-World Assumptions](Multi-Scenario/Rethinking%20Cross-Domain%20Sequential%20Recommendation%20under%20Open-World%20Assumptions.pdf) - [SAMD - An Industrial Framework for Heterogeneous Multi-Scenario Recommendation](Multi-Scenario/SAMD%20-%20An%20Industrial%20Framework%20for%20Heterogeneous%20Multi-Scenario%20Recommendation.pdf) - [SAR-Net - A Scenario-Aware Ranking Network for Personalized Fair Recommendation in Hundreds of Travel Scenarios](Multi-Scenario/SAR-Net%20-%20A%20Scenario-Aware%20Ranking%20Network%20for%20Personalized%20Fair%20Recommendation%20in%20Hundreds%20of%20Travel%20Scenarios.pdf) - [Scenario-Adaptive Feature Interaction for Click-Through Rate Prediction](Multi-Scenario/Scenario-Adaptive%20Feature%20Interaction%20for%20Click-Through%20Rate%20Prediction.pdf) - [Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendation](Multi-Scenario/Scenario-Aware%20Hierarchical%20Dynamic%20Network%20for%20Multi-Scenario%20Recommendation.pdf) - [Semi-Supervised Learning for Cross-Domain Recommendation to Cold-Start Users](Multi-Scenario/Semi-Supervised%20Learning%20for%20Cross-Domain%20Recommendation%20to%20Cold-Start%20Users.pdf) - [Scenario-aware and Mutual-based approach for Multi-scenario Recommendation in E-Commerce](Multi-Scenario/Scenario-aware%20and%20Mutual-based%20approach%20for%20Multi-scenario%20Recommendation%20in%20E-Commerce.pdf) - [Scenario-Adaptive Fine-Grained Personalization Network- Tailoring User Behavior Representation to the Scenario Context](Multi-Scenario/Scenario-Adaptive%20Fine-Grained%20Personalization%20Network-%20Tailoring%20User%20Behavior%20Representation%20to%20the%20Scenario%20Context.pdf) - [Self-Supervised Learning on Users’ Spontaneous Behaviors for Multi-Scenario Ranking in E-commerce](Multi-Scenario/Self-Supervised%20Learning%20on%20Users%E2%80%99%20Spontaneous%20Behaviors%20for%20Multi-Scenario%20Ranking%20in%20E-commerce.pdf) - [Scenario-Adaptive and Self-Supervised Model for Multi-Scenario Personalized Recommendation](Multi-Scenario/Scenario-Adaptive%20and%20Self-Supervised%20Model%20for%20Multi-Scenario%20Personalized%20Recommendation.pdf) ## Debias - [[2019][Huawei][PAL] a position-bias aware learning framework for CTR prediction in live recommender systems](Debias/%5B2019%5D%5BHuawei%5D%5BPAL%5D%20a%20position-bias%20aware%20learning%20framework%20for%20CTR%20prediction%20in%20live%20recommender%20systems.pdf) - [[2020][Alibaba][ESAM] ESAM - Discriminative Domain Adaptation with Non-Displayed Items to Improve Long-Tail Performance](Debias/%5B2020%5D%5BAlibaba%5D%5BESAM%5D%20ESAM%20-%20Discriminative%20Domain%20Adaptation%20with%20Non-Displayed%20Items%20to%20Improve%20Long-Tail%20Performance.pdf) - [Are You Influenced by Others When Rating? Improve Rating Prediction by Conformity Modeling](Debias/Are%20You%20Influenced%20by%20Others%20When%20Rating%3F%20Improve%20Rating%20Prediction%20by%20Conformity%20Modeling.pdf) - [A General Knowledge Distillation Framework for Counterfactual Recommendation via Uniform Data](Debias/A%20General%20Knowledge%20Distillation%20Framework%20for%20Counterfactual%20Recommendation%20via%20Uniform%20Data.pdf) - [AutoDebias - Learning to Debias for Recommendation](Debias/AutoDebias%20-%20Learning%20to%20Debias%20for%20Recommendation.pdf) - [Bias and Debias in Recommender System - A Survey and Future Directions](Debias/Bias%20and%20Debias%20in%20Recommender%20System%20-%20A%20Survey%20and%20Future%20Directions.pdf) - [Co-training Disentangled Domain Adaptation Network for Leveraging Popularity Bias in Recommenders](Debias/Co-training%20Disentangled%20Domain%20Adaptation%20Network%20for%20Leveraging%20Popularity%20Bias%20in%20Recommenders.pdf) - [Counterfactual Video Recommendation for Duration Debiasing](Debias/Counterfactual%20Video%20Recommendation%20for%20Duration%20Debiasing.pdf) - [Causal Intervention for Leveraging Popularity Bias in Recommendation](Debias/Causal%20Intervention%20for%20Leveraging%20Popularity%20Bias%20in%20Recommendation.pdf) - [Deep Position-wise Interaction Network for CTR Prediction](Debias/Deep%20Position-wise%20Interaction%20Network%20for%20CTR%20Prediction.pdf) - [Debiased Recommendation with User Feature Balancing](Debias/Debiased%20Recommendation%20with%20User%20Feature%20Balancing.pdf) - [Debiasing the Human-Recommender System Feedback Loop in Collaborative Filtering](Debias/Debiasing%20the%20Human-Recommender%20System%20Feedback%20Loop%20in%20Collaborative%20Filtering.pdf) - [Denoising Implicit Feedback for Recommendation](Debias/Denoising%20Implicit%20Feedback%20for%20Recommendation.pdf) - [DVR - Micro-Video Recommendation Optimizing Watch-Time-Gain under Duration Bias](Debias/DVR%20-%20Micro-Video%20Recommendation%20Optimizing%20Watch-Time-Gain%20under%20Duration%20Bias.pdf) - [Deconvolving Feedback Loops in Recommender Systems](Debias/Deconvolving%20Feedback%20Loops%20in%20Recommender%20Systems.pdf) - [Disentangling User Interest and Conformity for Recommendation with Causal Embedding](Debias/Disentangling%20User%20Interest%20and%20Conformity%20for%20Recommendation%20with%20Causal%20Embedding.pdf) - [Degenerate Feedback Loops in Recommender Systems](Debias/Degenerate%20Feedback%20Loops%20in%20Recommender%20Systems.pdf) - [Influence Function for Unbiased Recommendation](Debias/Influence%20Function%20for%20Unbiased%20Recommendation.pdf) - [Improving Ad Click Prediction by Considering Non-displayed Events](Debias/Improving%20Ad%20Click%20Prediction%20by%20Considering%20Non-displayed%20Events.pdf) - [Improving Micro-video Recommendation by Controlling Position Bias](Debias/Improving%20Micro-video%20Recommendation%20by%20Controlling%20Position%20Bias.pdf) - [Learning to rank with selection bias in personal search](Debias/Learning%20to%20rank%20with%20selection%20bias%20in%20personal%20search.pdf) - [Predicting Counterfactuals from Large Historical Data and Small Randomized Trials](Debias/Predicting%20Counterfactuals%20from%20Large%20Historical%20Data%20and%20Small%20Randomized%20Trials.pdf) - [Recommendations as Treatments - Debiasing Learning and Evaluation](Debias/Recommendations%20as%20Treatments%20-%20Debiasing%20Learning%20and%20Evaluation.pdf) - [Rec4Ad - A Free Lunch to Mitigate Sample Selection Bias for Ads CTR Prediction in Taobao](Debias/Rec4Ad%20-%20A%20Free%20Lunch%20to%20Mitigate%20Sample%20Selection%20Bias%20for%20Ads%20CTR%20Prediction%20in%20Taobao.pdf) - [Should I Follow the Crowd? A Probabilistic Analysis of the Effectiveness of Popularity in Recommender Systems](Debias/Should%20I%20Follow%20the%20Crowd%3F%20A%20Probabilistic%20Analysis%20of%20the%20Effectiveness%20of%20Popularity%20in%20Recommender%20Systems.pdf) - [Training and Testing of Recommender Systems on Data Missing Not at Random](Debias/Training%20and%20Testing%20of%20Recommender%20Systems%20on%20Data%20Missing%20Not%20at%20Random.pdf) - [UKD - Debiasing Conversion Rate Estimation via Uncertainty-regularized Knowledge Distillation](Debias/UKD%20-%20Debiasing%20Conversion%20Rate%20Estimation%20via%20Uncertainty-regularized%20Knowledge%20Distillation.pdf) - [Uncovering User Interest from Biased and Noised Watch Time in Video Recommendation](Debias/Uncovering%20User%20Interest%20from%20Biased%20and%20Noised%20Watch%20Time%20in%20Video%20Recommendation.pdf) - [Unbiased Learning-to-Rank with Biased Feedback](Debias/Unbiased%20Learning-to-Rank%20with%20Biased%20Feedback.pdf) ## Calibration - [Attended Temperature Scaling - A Practical Approach for Calibrating Deep Neural Networks](Calibration/Attended%20Temperature%20Scaling%20-%20A%20Practical%20Approach%20for%20Calibrating%20Deep%20Neural%20Networks.pdf) - [A Self-boosted Framework for Calibrated Ranking](Calibration/A%20Self-boosted%20Framework%20for%20Calibrated%20Ranking.pdf) - [Beyond temperature scaling - Obtaining well-calibrated multiclass probabilities with Dirichlet calibration](Calibration/Beyond%20temperature%20scaling%20-%20Obtaining%20well-calibrated%20multiclass%20probabilities%20with%20Dirichlet%20calibration.pdf) - [Beta calibration - a well-founded and easily implemented improvement on logistic calibration for binary classifiers](Calibration/Beta%20calibration%20-%20a%20well-founded%20and%20easily%20implemented%20improvement%20on%20logistic%20calibration%20for%20binary%20classifiers.pdf) - [CALIBRATION OF NEURAL NETWORKS USING SPLINES](Calibration/CALIBRATION%20OF%20NEURAL%20NETWORKS%20USING%20SPLINES.pdf) - [Calibrating User Response Predictions in Online Advertising](Calibration/Calibrating%20User%20Response%20Predictions%20in%20Online%20Advertising.pdf) - [Crank up the volume - preference bias amplificationin collaborative recommendation](Calibration/Crank%20up%20the%20volume%20-%20preference%20bias%20amplificationin%20collaborative%20recommendation.pdf) - [Deep Ensemble Shape Calibration - Multi-Field Post-hoc Calibration in Online Advertising](Calibration/Deep%20Ensemble%20Shape%20Calibration%20-%20Multi-Field%20Post-hoc%20Calibration%20in%20Online%20Advertising.pdf) - [Distribution-free calibration guarantees for histogram binning without sample splitting](Calibration/Distribution-free%20calibration%20guarantees%20for%20histogram%20binning%20without%20sample%20splitting.pdf) - [Field-aware Calibration - A Simple and Empirically Strong Method for Reliable Probabilistic Predictions](Calibration/Field-aware%20Calibration%20-%20A%20Simple%20and%20Empirically%20Strong%20Method%20for%20Reliable%20Probabilistic%20Predictions.pdf) - [Mitigating Bias in Calibration Error Estimation](Calibration/Mitigating%20Bias%20in%20Calibration%20Error%20Estimation.pdf) - [Measuring Calibration in Deep Learning](Calibration/Measuring%20Calibration%20in%20Deep%20Learning.pdf) - [MBCT - Tree-Based Feature-Aware Binning for Individual Uncertainty Calibration](Calibration/MBCT%20-%20Tree-Based%20Feature-Aware%20Binning%20for%20Individual%20Uncertainty%20Calibration.pdf) - [On Calibration of Modern Neural Networks](Calibration/On%20Calibration%20of%20Modern%20Neural%20Networks.pdf) - [Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers](Calibration/Obtaining%20calibrated%20probability%20estimates%20from%20decision%20trees%20and%20naive%20Bayesian%20classifiers.pdf) - [Obtaining Well Calibrated Probabilities Using Bayesian Binning](Calibration/Obtaining%20Well%20Calibrated%20Probabilities%20Using%20Bayesian%20Binning.pdf) - [Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods](Calibration/Probabilistic%20outputs%20for%20support%20vector%20machines%20and%20comparisons%20to%20regularized%20likelihood%20methods.pdf) - [Posterior Probability Matters - Doubly-Adaptive Calibration for Neural Predictions in Online Advertising](Calibration/Posterior%20Probability%20Matters%20-%20Doubly-Adaptive%20Calibration%20for%20Neural%20Predictions%20in%20Online%20Advertising.pdf) - [Regression Compatible Listwise Objectives for Calibrated Ranking with Binary Relevance](Calibration/Regression%20Compatible%20Listwise%20Objectives%20for%20Calibrated%20Ranking%20with%20Binary%20Relevance.pdf) - [Transforming Classifier Scores into Accurate Multiclass Probability Estimates](Calibration/Transforming%20Classifier%20Scores%20into%20Accurate%20Multiclass%20Probability%20Estimates.pdf) - [Unconstrained Monotonic Calibration of Predictions in Deep Ranking Systems](Calibration/Unconstrained%20Monotonic%20Calibration%20of%20Predictions%20in%20Deep%20Ranking%20Systems.pdf) ## Distillation - [[2021][Tencent][DMTL] Distillation based Multi-task Learning - A Candidate Generation Model for Improving Reading Duration](Distillation/%5B2021%5D%5BTencent%5D%5BDMTL%5D%20Distillation%20based%20Multi-task%20Learning%20-%20A%20Candidate%20Generation%20Model%20for%20Improving%20Reading%20Duration.pdf) - [Ensembled CTR Prediction via Knowledge Distillation](Distillation/Ensembled%20CTR%20Prediction%20via%20Knowledge%20Distillation.pdf) - [Privileged Features Distillation at Taobao Recommendations](Distillation/Privileged%20Features%20Distillation%20at%20Taobao%20Recommendations.pdf) - [Rocket Launching - A Universal and Efficient Framework for Training Well-performing Light Net](Distillation/Rocket%20Launching%20-%20A%20Universal%20and%20Efficient%20Framework%20for%20Training%20Well-performing%20Light%20Net.pdf) - [Ranking Distillation - Learning Compact Ranking Models With High Performance for Recommender System](Distillation/Ranking%20Distillation%20-%20Learning%20Compact%20Ranking%20Models%20With%20High%20Performance%20for%20Recommender%20System.pdf) - [Unbiased Knowledge Distillation for Recommendation](Distillation/Unbiased%20Knowledge%20Distillation%20for%20Recommendation.pdf) ## Feedback-Delay - [[2021][Alibaba] Real Negatives Matter - Continuous Training with Real Negatives for Delayed Feedback Modeling](Feedback-Delay/%5B2021%5D%5BAlibaba%5D%20Real%20Negatives%20Matter%20-%20Continuous%20Training%20with%20Real%20Negatives%20for%20Delayed%20Feedback%20Modeling.pdf) - [Asymptotically Unbiased Estimation for Delayed Feedback Modeling via Label Correction](Feedback-Delay/Asymptotically%20Unbiased%20Estimation%20for%20Delayed%20Feedback%20Modeling%20via%20Label%20Correction.pdf) - [An Attention-based Model for Conversion Rate Prediction with Delayed Feedback via Post-click Calibration](Feedback-Delay/An%20Attention-based%20Model%20for%20Conversion%20Rate%20Prediction%20with%20Delayed%20Feedback%20via%20Post-click%20Calibration.pdf) - [Addressing Delayed Feedback for Continuous Training with Neural Networks in CTR prediction](Feedback-Delay/Addressing%20Delayed%20Feedback%20for%20Continuous%20Training%20with%20Neural%20Networks%20in%20CTR%20prediction.pdf) - [A Nonparametric Delayed Feedback Model for Conversion Rate Prediction](Feedback-Delay/A%20Nonparametric%20Delayed%20Feedback%20Model%20for%20Conversion%20Rate%20Prediction.pdf) - [A Feedback Shift Correction in Predicting Conversion Rates under Delayed Feedback](Feedback-Delay/A%20Feedback%20Shift%20Correction%20in%20Predicting%20Conversion%20Rates%20under%20Delayed%20Feedback.pdf) - [A Multi-Task Learning Approach for Delayed Feedback Modeling](Feedback-Delay/A%20Multi-Task%20Learning%20Approach%20for%20Delayed%20Feedback%20Modeling.pdf) - [Counterfactual Reward Modification for Streaming Recommendation with Delayed Feedback](Feedback-Delay/Counterfactual%20Reward%20Modification%20for%20Streaming%20Recommendation%20with%20Delayed%20Feedback.pdf) - [Capturing Delayed Feedback in Conversion Rate Predictionvia Elapsed-Time Sampling](Feedback-Delay/Capturing%20Delayed%20Feedback%20in%20Conversion%20Rate%20Predictionvia%20Elapsed-Time%20Sampling.pdf) - [Delayed Feedback Model with Negative Binomial Regression for Multiple Conversions](Feedback-Delay/Delayed%20Feedback%20Model%20with%20Negative%20Binomial%20Regression%20for%20Multiple%20Conversions.pdf) - [Delayed Feedback Modeling for the Entire Space Conversion Rate Prediction](Feedback-Delay/Delayed%20Feedback%20Modeling%20for%20the%20Entire%20Space%20Conversion%20Rate%20Prediction.pdf) - [Dual Learning Algorithm for Delayed Conversions](Feedback-Delay/Dual%20Learning%20Algorithm%20for%20Delayed%20Conversions.pdf) - [Entire Space Cascade Delayed Feedback Modeling for Effective Conversion Rate Prediction](Feedback-Delay/Entire%20Space%20Cascade%20Delayed%20Feedback%20Modeling%20for%20Effective%20Conversion%20Rate%20Prediction.pdf) - [Generalized Delayed Feedback Model with Post-Click Information in Recommender Systems](Feedback-Delay/Generalized%20Delayed%20Feedback%20Model%20with%20Post-Click%20Information%20in%20Recommender%20Systems.pdf) - [Handling many conversions per click in modeling delayed feedback](Feedback-Delay/Handling%20many%20conversions%20per%20click%20in%20modeling%20delayed%20feedback.pdf) - [Modeling Delayed Feedback in Display Advertising](Feedback-Delay/Modeling%20Delayed%20Feedback%20in%20Display%20Advertising.pdf) ## ContrastiveLearning - [Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation](ContrastiveLearning/Are%20Graph%20Augmentations%20Necessary%3F%20Simple%20Graph%20Contrastive%20Learning%20for%20Recommendation.pdf) - [An Empirical Study of Training Self-Supervised Vision Transformers](ContrastiveLearning/An%20Empirical%20Study%20of%20Training%20Self-Supervised%20Vision%20Transformers.pdf) - [A Simple Framework for Contrastive Learning of Visual Representations](ContrastiveLearning/A%20Simple%20Framework%20for%20Contrastive%20Learning%20of%20Visual%20Representations.pdf) - [Bootstrap Your Own Latent A New Approach to Self-Supervised Learning](ContrastiveLearning/Bootstrap%20Your%20Own%20Latent%20A%20New%20Approach%20to%20Self-Supervised%20Learning.pdf) - [Contrastive Learning for Conversion Rate Prediction](ContrastiveLearning/Contrastive%20Learning%20for%20Conversion%20Rate%20Prediction.pdf) - [Contrastive Learning for Interactive Recommendation in Fashion](ContrastiveLearning/Contrastive%20Learning%20for%20Interactive%20Recommendation%20in%20Fashion.pdf) - [CL4CTR - A Contrastive Learning Framework for CTR Prediction](ContrastiveLearning/CL4CTR%20-%20A%20Contrastive%20Learning%20Framework%20for%20CTR%20Prediction.pdf) - [Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems](ContrastiveLearning/Contrastive%20Learning%20for%20Debiased%20Candidate%20Generation%20in%20Large-Scale%20Recommender%20Systems.pdf) - [Contrastive Self-supervised Sequential Recommendation with Robust Augmentation](ContrastiveLearning/Contrastive%20Self-supervised%20Sequential%20Recommendation%20with%20Robust%20Augmentation.pdf) - [CCL4Rec - Contrast over Contrastive Learning for Micro-video Recommendation](ContrastiveLearning/CCL4Rec%20-%20Contrast%20over%20Contrastive%20Learning%20for%20Micro-video%20Recommendation.pdf) - [Disentangled Causal Embedding With Contrastive Learning For Recommender System](ContrastiveLearning/Disentangled%20Causal%20Embedding%20With%20Contrastive%20Learning%20For%20Recommender%20System.pdf) - [Disentangled Contrastive Learning for Social Recommendation](ContrastiveLearning/Disentangled%20Contrastive%20Learning%20for%20Social%20Recommendation.pdf) - [Exploiting Negative Preference in Content-based Music Recommendation with Contrastive Learning](ContrastiveLearning/Exploiting%20Negative%20Preference%20in%20Content-based%20Music%20Recommendation%20with%20Contrastive%20Learning.pdf) - [Improving Knowledge-aware Recommendation with Multi-level Interactive Contrastive Learning](ContrastiveLearning/Improving%20Knowledge-aware%20Recommendation%20with%20Multi-level%20Interactive%20Contrastive%20Learning.pdf) - [Improved Baselines with Momentum Contrastive Learning](ContrastiveLearning/Improved%20Baselines%20with%20Momentum%20Contrastive%20Learning.pdf) - [Multi-view Multi-behavior Contrastive Learning in Recommendation](ContrastiveLearning/Multi-view%20Multi-behavior%20Contrastive%20Learning%20in%20Recommendation.pdf) - [Momentum Contrast for Unsupervised Visual Representation Learning](ContrastiveLearning/Momentum%20Contrast%20for%20Unsupervised%20Visual%20Representation%20Learning.pdf) - [Multi-level Contrastive Learning Framework for Sequential Recommendation](ContrastiveLearning/Multi-level%20Contrastive%20Learning%20Framework%20for%20Sequential%20Recommendation.pdf) - [Predictive and Contrastive- Dual-Auxiliary Learning for Recommendation](ContrastiveLearning/Predictive%20and%20Contrastive-%20Dual-Auxiliary%20Learning%20for%20Recommendation.pdf) - [Taming the One-Epoch Phenomenon in Online Recommendation System by Two-stage Contrastive ID Pre-training](ContrastiveLearning/Taming%20the%20One-Epoch%20Phenomenon%20in%20Online%20Recommendation%20System%20by%20Two-stage%20Contrastive%20ID%20Pre-training.pdf) - [Understanding the Behaviour of Contrastive Loss](ContrastiveLearning/Understanding%20the%20Behaviour%20of%20Contrastive%20Loss.pdf) - [Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere](ContrastiveLearning/Understanding%20Contrastive%20Representation%20Learning%20through%20Alignment%20and%20Uniformity%20on%20the%20Hypersphere.pdf) ## Cold-Start - [[2017][MAML] Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](Cold-Start/%5B2017%5D%5BMAML%5D%20Model-Agnostic%20Meta-Learning%20for%20Fast%20Adaptation%20of%20Deep%20Networks.pdf) - [[2017][DropoutNet] DropoutNet - Addressing Cold Start in Recommender Systems](Cold-Start/%5B2017%5D%5BDropoutNet%5D%20DropoutNet%20-%20Addressing%20Cold%20Start%20in%20Recommender%20Systems.pdf) - [[2017][HIN] Heterogeneous Information Network Embedding for Recommendation](Cold-Start/%5B2017%5D%5BHIN%5D%20Heterogeneous%20Information%20Network%20Embedding%20for%20Recommendation.pdf) - [[2019][Microsoft][CB2CF] CB2CF - A Neural Multiview Content-to-Collaborative Filtering Model for Completely Cold Item Recommendations](Cold-Start/%5B2019%5D%5BMicrosoft%5D%5BCB2CF%5D%20CB2CF%20-%20A%20Neural%20Multiview%20Content-to-Collaborative%20Filtering%20Model%20for%20Completely%20Cold%20Item%20Recommendations.pdf) - [[2020][Wechat][ICAN] Internal and Contextual Attention Network for Cold-start Multi-channel Matching in Recommendation](Cold-Start/%5B2020%5D%5BWechat%5D%5BICAN%5D%20Internal%20and%20Contextual%20Attention%20Network%20for%20Cold-start%20Multi-channel%20Matching%20in%20Recommendation.pdf) - [[2021][Kuaishou][POSO] POSO - Personalized Cold Start Modules for Large-scale Recommender Systems](Cold-Start/%5B2021%5D%5BKuaishou%5D%5BPOSO%5D%20POSO%20-%20Personalized%20Cold%20Start%20Modules%20for%20Large-scale%20Recommender%20Systems.pdf) - [Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework](Cold-Start/Alleviating%20Cold-start%20Problem%20in%20CTR%20Prediction%20with%20A%20Variational%20Embedding%20Learning%20Framework.pdf) - [An Unified Search and Recommendation Foundation Model for Cold-Start Scenario](Cold-Start/An%20Unified%20Search%20and%20Recommendation%20Foundation%20Model%20for%20Cold-Start%20Scenario.pdf) - [Addressing the Item Cold-start Problem by Attribute-driven Active Learning](Cold-Start/Addressing%20the%20Item%20Cold-start%20Problem%20by%20Attribute-driven%20Active%20Learning.pdf) - [A Practical Exploration System for Search Advertising](Cold-Start/A%20Practical%20Exploration%20System%20for%20Search%20Advertising.pdf) - [A Model of Two Tales - Dual Transfer Learning Framework for Improved Long-tail Item Recommendation](Cold-Start/A%20Model%20of%20Two%20Tales%20-%20Dual%20Transfer%20Learning%20Framework%20for%20Improved%20Long-tail%20Item%20Recommendation.pdf) - [A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps](Cold-Start/A%20Semi-Personalized%20System%20for%20User%20Cold%20Start%20Recommendation%20on%20Music%20Streaming%20Apps.pdf) - [A Multi-modal Modeling Framework for Cold-start Short-video Recommendation](Cold-Start/A%20Multi-modal%20Modeling%20Framework%20for%20Cold-start%20Short-video%20Recommendation.pdf) - [Bootstrapping Contrastive Learning Enhanced Music Cold-Start Matching](Cold-Start/Bootstrapping%20Contrastive%20Learning%20Enhanced%20Music%20Cold-Start%20Matching.pdf) - [Contrastive Learning for Cold-Start Recommendation](Cold-Start/Contrastive%20Learning%20for%20Cold-Start%20Recommendation.pdf) - [Cold-start Sequential Recommendation via Meta Learner](Cold-Start/Cold-start%20Sequential%20Recommendation%20via%20Meta%20Learner.pdf) - [Cold-Starting Podcast Ads and Promotions with Multi-Task Learning on Spotify](Cold-Start/Cold-Starting%20Podcast%20Ads%20and%20Promotions%20with%20Multi-Task%20Learning%20on%20Spotify.pdf) - [Contrastive Collaborative Filtering for Cold-Start Item Recommendation](Cold-Start/Contrastive%20Collaborative%20Filtering%20for%20Cold-Start%20Item%20Recommendation.pdf) - [Cold & Warm Net - Addressing Cold-Start Users in Recommender Systems](Cold-Start/Cold%20%26%20Warm%20Net%20-%20Addressing%20Cold-Start%20Users%20in%20Recommender%20Systems.pdf) - [Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN)](Cold-Start/Empowering%20Long-tail%20Item%20Recommendation%20through%20Cross%20Decoupling%20Network%20%28CDN%29.pdf) - [Fresh Content Needs More Attention - Multi-funnel Fresh Content Recommendation](Cold-Start/Fresh%20Content%20Needs%20More%20Attention%20-%20Multi-funnel%20Fresh%20Content%20Recommendation.pdf) - [GIFT - Graph-guIded Feature Transfer for Cold-Start Video Click-Through Rate Prediction](Cold-Start/GIFT%20-%20Graph-guIded%20Feature%20Transfer%20for%20Cold-Start%20Video%20Click-Through%20Rate%20Prediction.pdf) - [Handling User Cold Start Problem in Recommender Systems Using Fuzzy Clustering](Cold-Start/Handling%20User%20Cold%20Start%20Problem%20in%20Recommender%20Systems%20Using%20Fuzzy%20Clustering.pdf) - [Is Meta-Learning the Right Approach for the Cold-Start Problem in Recommender Systems?](Cold-Start/Is%20Meta-Learning%20the%20Right%20Approach%20for%20the%20Cold-Start%20Problem%20in%20Recommender%20Systems%3F.pdf) - [Improving Item Cold-start Recommendation via Model-agnostic Conditional Variational Autoencoder](Cold-Start/Improving%20Item%20Cold-start%20Recommendation%20via%20Model-agnostic%20Conditional%20Variational%20Autoencoder.pdf) - [Long-tail Augmented Graph Contrastive Learning for Recommendation](Cold-Start/Long-tail%20Augmented%20Graph%20Contrastive%20Learning%20for%20Recommendation.pdf) - [Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks](Cold-Start/Learning%20to%20Warm%20Up%20Cold%20Item%20Embeddings%20for%20Cold-start%20Recommendation%20with%20Meta%20Scaling%20and%20Shifting%20Networks.pdf) - [Long-Tail Learning via Logit Adjustment](Cold-Start/Long-Tail%20Learning%20via%20Logit%20Adjustment.pdf) - [LHRM - A LBS based Heterogeneous Relations Model for User Cold Start Recommendation in Online Travel Platform](Cold-Start/LHRM%20-%20A%20LBS%20based%20Heterogeneous%20Relations%20Model%20for%20User%20Cold%20Start%20Recommendation%20in%20Online%20Travel%20Platform.pdf) - [MAMO - Memory-Augmented Meta-Optimization for Cold-start Recommendation](Cold-Start/MAMO%20-%20Memory-Augmented%20Meta-Optimization%20for%20Cold-start%20Recommendation.pdf) - [Next-User Retrieva l- Enhancing Cold-Start Recommendations via Generative Next-User Modeling](Cold-Start/Next-User%20Retrieva%20l-%20Enhancing%20Cold-Start%20Recommendations%20via%20Generative%20Next-User%20Modeling.pdf) - [Neighbor Based Enhancement for the Long-Tail Ranking Problem in Video Rank Models](Cold-Start/Neighbor%20Based%20Enhancement%20for%20the%20Long-Tail%20Ranking%20Problem%20in%20Video%20Rank%20Models.pdf) - [Online Item Cold-Start Recommendation with Popularity-Aware Meta-Learning](Cold-Start/Online%20Item%20Cold-Start%20Recommendation%20with%20Popularity-Aware%20Meta-Learning.pdf) - [SocRipple - A Two-Stage Framework for Cold-Start Video Recommendations](Cold-Start/SocRipple%20-%20A%20Two-Stage%20Framework%20for%20Cold-Start%20Video%20Recommendations.pdf) - [SaviorRec - Semantic-Behavior Alignment for Cold-Start Recommendation](Cold-Start/SaviorRec%20-%20Semantic-Behavior%20Alignment%20for%20Cold-Start%20Recommendation.pdf) - [SimRec - Mitigating the Cold-Start Problem in Sequential Recommendation by Integrating Item Similarity](Cold-Start/SimRec%20-%20Mitigating%20the%20Cold-Start%20Problem%20in%20Sequential%20Recommendation%20by%20Integrating%20Item%20Similarity.pdf) - [SMINet - State-Aware Multi-Aspect Interests Representation Network for Cold-Start Users Recommendation](Cold-Start/SMINet%20-%20State-Aware%20Multi-Aspect%20Interests%20Representation%20Network%20for%20Cold-Start%20Users%20Recommendation.pdf) - [Task-adaptive Neural Process for User Cold-Start Recommendation](Cold-Start/Task-adaptive%20Neural%20Process%20for%20User%20Cold-Start%20Recommendation.pdf) - [Transform Cold-Start Users into Warm via Fused Behaviors in Large-Scale Recommendation](Cold-Start/Transform%20Cold-Start%20Users%20into%20Warm%20via%20Fused%20Behaviors%20in%20Large-Scale%20Recommendation.pdf) - [Telepath - Understanding Users from a Human Vision Perspective in Large-Scale Recommender Systems](Cold-Start/Telepath%20-%20Understanding%20Users%20from%20a%20Human%20Vision%20Perspective%20in%20Large-Scale%20Recommender%20Systems.pdf) - [Value of Exploration - Measurements, Findings and Algorithms](Cold-Start/Value%20of%20Exploration%20-%20Measurements%2C%20Findings%20and%20Algorithms.pdf) - [Warming Up Cold-Start CTR Prediction by Learning Item-Specific Feature Interactions](Cold-Start/Warming%20Up%20Cold-Start%20CTR%20Prediction%20by%20Learning%20Item-Specific%20Feature%20Interactions.pdf) - [Warmer for Less - A Cost-Efficient Strategy for Cold-Start Recommendations at Pinterest](Cold-Start/Warmer%20for%20Less%20-%20A%20Cost-Efficient%20Strategy%20for%20Cold-Start%20Recommendations%20at%20Pinterest.pdf) - [Warm Up Cold-start Advertisements - Improving CTR Predictions via Learning to Learn ID Embeddings](Cold-Start/Warm%20Up%20Cold-start%20Advertisements%20-%20Improving%20CTR%20Predictions%20via%20Learning%20to%20Learn%20ID%20Embeddings.pdf) #### Exploration&Exploitation - [An Empirical Evaluation of Thompson Sampling](Cold-Start/Exploration%26Exploitation/An%20Empirical%20Evaluation%20of%20Thompson%20Sampling.pdf) - [Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction](Cold-Start/Exploration%26Exploitation/Adversarial%20Gradient%20Driven%20Exploration%20for%20Deep%20Click-Through%20Rate%20Prediction.pdf) - [A Contextual-Bandit Approach to Personalized News Article Recommendation](Cold-Start/Exploration%26Exploitation/A%20Contextual-Bandit%20Approach%20to%20Personalized%20News%20Article%20Recommendation.pdf) - [Blocked Collaborative Bandits - Online Collaborative Filtering with Per-Item Budget Constraints](Cold-Start/Exploration%26Exploitation/Blocked%20Collaborative%20Bandits%20-%20Online%20Collaborative%20Filtering%20with%20Per-Item%20Budget%20Constraints.pdf) - [Comparison-based Conversational Recommender System with Relative Bandit Feedback](Cold-Start/Exploration%26Exploitation/Comparison-based%20Conversational%20Recommender%20System%20with%20Relative%20Bandit%20Feedback.pdf) - [Efficient Sparse Linear Bandits under High Dimensional Data](Cold-Start/Exploration%26Exploitation/Efficient%20Sparse%20Linear%20Bandits%20under%20High%20Dimensional%20Data.pdf) - [Scalable Neural Contextual Bandit for Recommender Systems](Cold-Start/Exploration%26Exploitation/Scalable%20Neural%20Contextual%20Bandit%20for%20Recommender%20Systems.pdf) #### MetaLearning - [A Meta-Learning Perspective on Cold-Start Recommendations for Items](Cold-Start/MetaLearning/A%20Meta-Learning%20Perspective%20on%20Cold-Start%20Recommendations%20for%20Items.pdf) - [Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate Prediction](Cold-Start/MetaLearning/Learning%20Graph%20Meta%20Embeddings%20for%20Cold-Start%20Ads%20in%20Click-Through%20Rate%20Prediction.pdf) - [Meta-learning on Heterogeneous Information Networks for Cold-start Recommendation](Cold-Start/MetaLearning/Meta-learning%20on%20Heterogeneous%20Information%20Networks%20for%20Cold-start%20Recommendation.pdf) - [MeLU - Meta-Learned User Preference Estimator for Cold-Start Recommendation](Cold-Start/MetaLearning/MeLU%20-%20Meta-Learned%20User%20Preference%20Estimator%20for%20Cold-Start%20Recommendation.pdf) - [Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks](Cold-Start/MetaLearning/Meta-Graph%20Based%20Recommendation%20Fusion%20over%20Heterogeneous%20Information%20Networks.pdf) - [Preference-Adaptive Meta-Learning for Cold-Start Recommendation](Cold-Start/MetaLearning/Preference-Adaptive%20Meta-Learning%20for%20Cold-Start%20Recommendation.pdf) - [Personalized Adaptive Meta Learning for Cold-start User Preference Prediction](Cold-Start/MetaLearning/Personalized%20Adaptive%20Meta%20Learning%20for%20Cold-start%20User%20Preference%20Prediction.pdf) - [Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start Users](Cold-Start/MetaLearning/Transfer-Meta%20Framework%20for%20Cross-domain%20Recommendation%20to%20Cold-Start%20Users.pdf) ## Learning-to-Rank - [AliExpress Learning-To-Rank- Maximizing Online Model Performance without Going Online](Learning-to-Rank/AliExpress%20Learning-To-Rank-%20Maximizing%20Online%20Model%20Performance%20without%20Going%20Online.pdf) - [Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks](Learning-to-Rank/Learning%20Groupwise%20Multivariate%20Scoring%20Functions%20Using%20Deep%20Neural%20Networks.pdf) #### Pair-wise - [LambdaRank - Learning to Rank with Nonsmooth Cost Functions](Learning-to-Rank/Pair-wise/LambdaRank%20-%20Learning%20to%20Rank%20with%20Nonsmooth%20Cost%20Functions.pdf) - [RankNET - Learning to Rank Using Gradient Descent](Learning-to-Rank/Pair-wise/RankNET%20-%20Learning%20to%20Rank%20Using%20Gradient%20Descent.pdf) - [RankBoost - An Effcient Boosting Algorithm for Combining Preferences](Learning-to-Rank/Pair-wise/RankBoost%20-%20An%20Effcient%20Boosting%20Algorithm%20for%20Combining%20Preferences.pdf) #### Point-wise - [Learning to Rank Using Classification and Gradient](Learning-to-Rank/Point-wise/Learning%20to%20Rank%20Using%20Classification%20and%20Gradient.pdf) #### List-wise - [AdaRank - A Boosting Algorithm for Information Retrieval](Learning-to-Rank/List-wise/AdaRank%20-%20A%20Boosting%20Algorithm%20for%20Information%20Retrieval.pdf) - [From RankNet to LambdaRank to LambdaMART](Learning-to-Rank/List-wise/From%20RankNet%20to%20LambdaRank%20to%20LambdaMART.pdf) - [LambdaMART - Adapting Boosting for Information Retrieval Measures](Learning-to-Rank/List-wise/LambdaMART%20-%20Adapting%20Boosting%20for%20Information%20Retrieval%20Measures.pdf) - [ListNet - Learning to Rank - From Pairwise Approach to Listwise Approach](Learning-to-Rank/List-wise/ListNet%20-%20Learning%20to%20Rank%20-%20From%20Pairwise%20Approach%20to%20Listwise%20Approach.pdf) - [RankFormer - Listwise Learning-to-Rank Using Listwide Labels](Learning-to-Rank/List-wise/RankFormer%20-%20Listwise%20Learning-to-Rank%20Using%20Listwide%20Labels.pdf) ## Fairness - [[2020][FairCo] Controlling Fairness and Bias in Dynamic Learning-to-Rank](Fairness/%5B2020%5D%5BFairCo%5D%20Controlling%20Fairness%20and%20Bias%20in%20Dynamic%20Learning-to-Rank.pdf) - [Equity of Attention - Amortizing Individual Fairness in Rankings](Fairness/Equity%20of%20Attention%20-%20Amortizing%20Individual%20Fairness%20in%20Rankings.pdf) - [Fairness in Recommendation Ranking through Pairwise Comparisons](Fairness/Fairness%20in%20Recommendation%20Ranking%20through%20Pairwise%20Comparisons.pdf) ## Look-Alike - [[2019][Tencent][RALM] Real-time Attention Based Look-alike Model for Recommender System](Look-Alike/%5B2019%5D%5BTencent%5D%5BRALM%5D%20Real-time%20Attention%20Based%20Look-alike%20Model%20for%20Recommender%20System.pdf) - [[2019][Pinterest] Finding Users Who Act Alike - Transfer Learning for Expanding](Look-Alike/%5B2019%5D%5BPinterest%5D%20Finding%20Users%20Who%20Act%20Alike%20-%20Transfer%20Learning%20for%20Expanding.pdf) - [A Sub-linear, Massive-scale Look-alike Audience Extension System](Look-Alike/A%20Sub-linear%2C%20Massive-scale%20Look-alike%20Audience%20Extension%20System.pdf) - [Adversarial Factorization Autoencoder for Look-alike Modeling](Look-Alike/Adversarial%20Factorization%20Autoencoder%20for%20Look-alike%20Modeling.pdf) - [A Feature-Pair-based Associative Classification Approach to Look-alike Modeling for Conversion-Oriented User-Targeting in Tail Campaigns](Look-Alike/A%20Feature-Pair-based%20Associative%20Classification%20Approach%20to%20Look-alike%20Modeling%20for%20Conversion-Oriented%20User-Targeting%20in%20Tail%20Campaigns.pdf) - [Audience Expansion for Online Social Network Advertising](Look-Alike/Audience%20Expansion%20for%20Online%20Social%20Network%20Advertising.pdf) - [Comprehensive Audience Expansion based on End-to-End Neural Prediction](Look-Alike/Comprehensive%20Audience%20Expansion%20based%20on%20End-to-End%20Neural%20Prediction.pdf) - [Effective Audience Extension in Online Advertising](Look-Alike/Effective%20Audience%20Extension%20in%20Online%20Advertising.pdf) - [Hubble - An Industrial System for Audience Expansion in Mobile Marketing](Look-Alike/Hubble%20-%20An%20Industrial%20System%20for%20Audience%20Expansion%20in%20Mobile%20Marketing.pdf) - [Implicit Look-alike Modelling in Display Ads - Transfer Collaborative Filtering to CTR Estimation ](Look-Alike/Implicit%20Look-alike%20Modelling%20in%20Display%20Ads%20-%20Transfer%20Collaborative%20Filtering%20to%20CTR%20Estimation%20.pdf) - [Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising](Look-Alike/Learning%20to%20Expand%20Audience%20via%20Meta%20Hybrid%20Experts%20and%20Critics%20for%20Recommendation%20and%20Advertising.pdf) - [Score Look-alike Audiences](Look-Alike/Score%20Look-alike%20Audiences.pdf) - [Two-Stage Audience Expansion for Financial Targeting in Marketing](Look-Alike/Two-Stage%20Audience%20Expansion%20for%20Financial%20Targeting%20in%20Marketing.pdf) ## CausalInference - [A Counterfactual Collaborative Session-based Recommender System](CausalInference/A%20Counterfactual%20Collaborative%20Session-based%20Recommender%20System.pdf) - [A Model-Agnostic Causal Learning Framework for Recommendation using Search Data](CausalInference/A%20Model-Agnostic%20Causal%20Learning%20Framework%20for%20Recommendation%20using%20Search%20Data.pdf) - [Addressing Confounding Feature Issue for Causal Recommendation](CausalInference/Addressing%20Confounding%20Feature%20Issue%20for%20Causal%20Recommendation.pdf) - [CausCF - Causal Collaborative Filtering for Recommendation Effect Estimation](CausalInference/CausCF%20-%20Causal%20Collaborative%20Filtering%20for%20Recommendation%20Effect%20Estimation.pdf) - [Coarse-to-fine Dynamic Uplift Modeling for Real-time Video Recommendation](CausalInference/Coarse-to-fine%20Dynamic%20Uplift%20Modeling%20for%20Real-time%20Video%20Recommendation.pdf) - [Causal Inference in Recommender Systems - A Survey of Strategies for Bias Mitigation, Explanation, and Generalization](CausalInference/Causal%20Inference%20in%20Recommender%20Systems%20-%20A%20Survey%20of%20Strategies%20for%20Bias%20%20Mitigation%2C%20Explanation%2C%20and%20Generalization.pdf) - [CauseRec - Counterfactual User Sequence Synthesis for Sequential Recommendation](CausalInference/CauseRec%20-%20Counterfactual%20User%20Sequence%20Synthesis%20for%20Sequential%20Recommendation.pdf) - [Counterfactual Data-Augmented Sequential Recommendation](CausalInference/Counterfactual%20Data-Augmented%20Sequential%20Recommendation.pdf) - [Clicks can be Cheating - Counterfactual Recommendation for Mitigating Clickbait Issue](CausalInference/Clicks%20can%20be%20Cheating%20-%20Counterfactual%20Recommendation%20for%20Mitigating%20Clickbait%20Issue.pdf) - [Counterfactual Learning-Driven Representation Disentanglement for Search-Enhanced Recommendation](CausalInference/Counterfactual%20Learning-Driven%20Representation%20Disentanglement%20for%20Search-Enhanced%20Recommendation.pdf) - [Causal Inference in Recommender Systems - A Survey and Future Directions](CausalInference/Causal%20Inference%20in%20Recommender%20Systems%20-%20A%20Survey%20and%20Future%20Directions.pdf) - [Doubly Robust Joint Learning for Recommendation on Data Missing Not at Random](CausalInference/Doubly%20Robust%20Joint%20Learning%20for%20Recommendation%20on%20Data%20Missing%20Not%20at%20Random.pdf) - [Deconfounded Recommendation for Alleviating Bias Amplification](CausalInference/Deconfounded%20Recommendation%20for%20Alleviating%20Bias%20Amplification.pdf) - [Heterogeneous Multi-treatment Uplift Modeling for Trade-off Optimization in Short-Video Recommendation](CausalInference/Heterogeneous%20Multi-treatment%20Uplift%20Modeling%20for%20Trade-off%20Optimization%20in%20Short-Video%20Recommendation.pdf) - [Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System](CausalInference/Model-Agnostic%20Counterfactual%20Reasoning%20for%20Eliminating%20Popularity%20Bias%20in%20Recommender%20System.pdf) - [Multi-Treatment Multi-Task Uplift Modeling for Enhancing User Growth](CausalInference/Multi-Treatment%20Multi-Task%20Uplift%20Modeling%20for%20Enhancing%20User%20Growth.pdf) - [Mitigating Hidden Confounding Effects for Causal Recommendation](CausalInference/Mitigating%20Hidden%20Confounding%20Effects%20for%20Causal%20Recommendation.pdf) - [On the Opportunity of Causal Learning in Recommendation Systems - Foundation, Estimation, Prediction and Challenges](CausalInference/On%20the%20Opportunity%20of%20Causal%20Learning%20in%20Recommendation%20Systems%20-%20Foundation%2C%20Estimation%2C%20Prediction%20and%20Challenges.pdf) - [Practical Counterfactual Policy Learning for Top-K Recommendations](CausalInference/Practical%20Counterfactual%20Policy%20Learning%20for%20Top-K%20Recommendations.pdf) - [Relative Contrastive Learning for Sequential Recommendation with Similarity-based Positive Pair Selection](CausalInference/Relative%20Contrastive%20Learning%20for%20Sequential%20Recommendation%20with%20Similarity-based%20Positive%20Pair%20Selection.pdf) - [Recommending the Most Effective Intervention to Improve Employment for Job Seekers with Disability](CausalInference/Recommending%20the%20Most%20Effective%20Intervention%20to%20Improve%20Employment%20for%20Job%20Seekers%20with%20Disability.pdf) - [Towards Unbiased and Robust Causal Ranking for Recommender Systems](CausalInference/Towards%20Unbiased%20and%20Robust%20Causal%20Ranking%20for%20Recommender%20Systems.pdf) - [Top-N Recommendation with Counterfactual User Preference Simulation](CausalInference/Top-N%20Recommendation%20with%20Counterfactual%20User%20Preference%20Simulation.pdf) - [Treatment Effect Estimation for User Interest Exploration on Recommender Systems](CausalInference/Treatment%20Effect%20Estimation%20for%20User%20Interest%20Exploration%20on%20Recommender%20Systems.pdf) ## Diversity - [[2020][Huawei][pDPP] Personalized Re-ranking for Improving Diversity in Live Recommender Systems](Diversity/%5B2020%5D%5BHuawei%5D%5BpDPP%5D%20Personalized%20Re-ranking%20for%20Improving%20Diversity%20in%20Live%20Recommender%20Systems.pdf) - [A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks](Diversity/A%20Framework%20for%20Recommending%20Accurate%20and%20Diverse%20Items%20Using%20Bayesian%20Graph%20Convolutional%20Neural%20Networks.pdf) - [A Survey of Diversification Techniques in Search and Recommendation](Diversity/A%20Survey%20of%20Diversification%20Techniques%20in%20Search%20and%20Recommendation.pdf) - [Adaptive, Personalized Diversity for Visual Discovery](Diversity/Adaptive%2C%20Personalized%20Diversity%20for%20Visual%20Discovery.pdf) - [Contextual Distillation Model for Diversified Recommendation](Diversity/Contextual%20Distillation%20Model%20for%20Diversified%20Recommendation.pdf) - [Calibrated Recommendations](Diversity/Calibrated%20Recommendations.pdf) - [Diversity on the Go! Streaming Determinantal Point Processes under a Maximum Induced Cardinality Objective](Diversity/Diversity%20on%20the%20Go%21%20Streaming%20Determinantal%20Point%20Processes%20under%20a%20Maximum%20Induced%20Cardinality%20Objective.pdf) - [DGCN - Diversified Recommendation with Graph Convolutional Networks](Diversity/DGCN%20-%20Diversified%20Recommendation%20with%20Graph%20Convolutional%20Networks.pdf) - [DGRec - Graph Neural Network for Recommendation with Diversified Embedding Generation](Diversity/DGRec%20-%20Graph%20Neural%20Network%20for%20Recommendation%20with%20Diversified%20Embedding%20Generation.pdf) - [Diversifying Search Results](Diversity/Diversifying%20Search%20Results.pdf) - [Enhancing Recommendation Diversity using Determinantal Point Processes on Knowledge Graphs](Diversity/Enhancing%20Recommendation%20Diversity%20using%20Determinantal%20Point%20Processes%20on%20Knowledge%20Graphs.pdf) - [Enhancing Domain-Level and User-Level Adaptivity in Diversified Recommendation](Diversity/Enhancing%20Domain-Level%20and%20User-Level%20Adaptivity%20in%20Diversified%20Recommendation.pdf) - [Exploiting Query Reformulations for Web Search Result Diversification](Diversity/Exploiting%20Query%20Reformulations%20for%20Web%20Search%20Result%20Diversification.pdf) - [Feature-aware Diversified Re-ranking with Disentangled Representations for Relevant Recommendation](Diversity/Feature-aware%20Diversified%20Re-ranking%20with%20Disentangled%20Representations%20for%20Relevant%20Recommendation.pdf) - [Future-Aware Diverse Trends Framework for Recommendation](Diversity/Future-Aware%20Diverse%20Trends%20Framework%20for%20Recommendation.pdf) - [Graph Exploration Matters- Improving both individual-level and system-level diversity in WeChat Feed Recommender](Diversity/Graph%20Exploration%20Matters-%20Improving%20both%20individual-level%20and%20system-level%20diversity%20in%20WeChat%20Feed%20Recommender.pdf) - [Improving Recommendation Lists Through Topic Diversification](Diversity/Improving%20Recommendation%20Lists%20Through%20Topic%20Diversification.pdf) - [Learning To Rank Diversely At Airbnb](Diversity/Learning%20To%20Rank%20Diversely%20At%20Airbnb.pdf) - [Multi-factor Sequential Re-ranking with Perception-Aware Diversification](Diversity/Multi-factor%20Sequential%20Re-ranking%20with%20Perception-Aware%20Diversification.pdf) - [Managing Diversity in Airbnb Search](Diversity/Managing%20Diversity%20in%20Airbnb%20Search.pdf) - [Novelty and Diversity in Information Retrieval Evaluation](Diversity/Novelty%20and%20Diversity%20in%20Information%20Retrieval%20Evaluation.pdf) - [P-Companion - A Principled Framework for Diversified Complementary Product Recommendation](Diversity/P-Companion%20-%20A%20Principled%20Framework%20for%20Diversified%20Complementary%20Product%20Recommendation.pdf) - [Sliding Spectrum Decomposition for Diversified Recommendation](Diversity/Sliding%20Spectrum%20Decomposition%20for%20Diversified%20Recommendation.pdf) - [User-controllable Recommendation Against Filter Bubbles](Diversity/User-controllable%20Recommendation%20Against%20Filter%20Bubbles.pdf) - [UNDERSTANDING DIVERSITY IN SESSION-BASED RECOMMENDATION](Diversity/UNDERSTANDING%20DIVERSITY%20IN%20SESSION-BASED%20RECOMMENDATION.pdf) ## ABTest - [All about Sample-Size Calculations for A:B Testing - Novel Extensions & Practical Guide](ABTest/All%20about%20Sample-Size%20Calculations%20for%20A%3AB%20Testing%20-%20Novel%20Extensions%20%26%20Practical%20Guide.pdf) - [Overlapping Experiment Infrastructure - More, Better, Faster Experimentation](ABTest/Overlapping%20Experiment%20Infrastructure%20-%20More%2C%20Better%2C%20Faster%20Experimentation.pdf) ## ReinforcementLearning - [A Reinforcement Learning Framework for Explainable Recommendation](ReinforcementLearning/A%20Reinforcement%20Learning%20Framework%20for%20Explainable%20Recommendation.pdf) - [Counterfactual Evaluation of Slate Recommendations with Sequential Reward Interactions](ReinforcementLearning/Counterfactual%20Evaluation%20of%20Slate%20Recommendations%20with%20Sequential%20Reward%20Interactions.pdf) - [DRN - A Deep Reinforcement Learning Framework for News Recommendation](ReinforcementLearning/DRN%20-%20A%20Deep%20Reinforcement%20Learning%20Framework%20for%20News%20Recommendation.pdf) - [Deep Reinforcement Learning for List-wise Recommendations](ReinforcementLearning/Deep%20Reinforcement%20Learning%20for%20List-wise%20Recommendations.pdf) - [Deep Reinforcement Learning for Search, Recommendation, and Online Advertising - A Survey](ReinforcementLearning/Deep%20Reinforcement%20Learning%20for%20Search%2C%20Recommendation%2C%20and%20Online%20Advertising%20-%20A%20Survey.pdf) - [Deep Reinforcement Learning for Page-wise Recommendations](ReinforcementLearning/Deep%20Reinforcement%20Learning%20for%20Page-wise%20Recommendations.pdf) - [Exploration and Regularization of the Latent Action Space in Recommendation](ReinforcementLearning/Exploration%20and%20Regularization%20of%20the%20Latent%20Action%20Space%20in%20Recommendation.pdf) - [InTune - Reinforcement Learning-based Data Pipeline Optimization for Deep Recommendation Models](ReinforcementLearning/InTune%20-%20Reinforcement%20Learning-based%20Data%20Pipeline%20Optimization%20for%20Deep%20Recommendation%20Models.pdf) - [Jointly Learning to Recommend and Advertise](ReinforcementLearning/Jointly%20Learning%20to%20Recommend%20and%20Advertise.pdf) - [Large-scale Interactive Recommendation with Tree-structured Policy Gradient](ReinforcementLearning/Large-scale%20Interactive%20Recommendation%20with%20Tree-structured%20Policy%20Gradient.pdf) - [Model-free Reinforcement Learning with Stochastic Reward Stabilization for Recommender Systems](ReinforcementLearning/Model-free%20Reinforcement%20Learning%20with%20Stochastic%20Reward%20Stabilization%20for%20Recommender%20Systems.pdf) - [Off-policy evaluation for slate recommendation](ReinforcementLearning/Off-policy%20evaluation%20for%20slate%20recommendation.pdf) - [Online Matching - A Real-time Bandit System for Large-scale Recommendations](ReinforcementLearning/Online%20Matching%20-%20A%20Real-time%20Bandit%20System%20for%20Large-scale%20Recommendations.pdf) - [Reinforcing User Retention in a Billion Scale Short Video Recommender System](ReinforcementLearning/Reinforcing%20User%20Retention%20in%20a%20Billion%20Scale%20Short%20Video%20Recommender%20System.pdf) - [Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning](ReinforcementLearning/Recommendations%20with%20Negative%20Feedback%20via%20Pairwise%20Deep%20Reinforcement%20Learning.pdf) - [Reinforcement Learning for Slate-based Recommender Systems - A Tractable Decomposition and Practical Methodology](ReinforcementLearning/Reinforcement%20Learning%20for%20Slate-based%20Recommender%20Systems%20-%20A%20Tractable%20Decomposition%20and%20Practical%20Methodology.pdf) - [Supervised Learning-enhanced Multi-Group Actor Critic for Live-stream Recommendation](ReinforcementLearning/Supervised%20Learning-enhanced%20Multi-Group%20Actor%20Critic%20for%20Live-stream%20Recommendation.pdf) - [Stabilizing Reinforcement Learning in Dynamic Environment with Application to Online Recommendation](ReinforcementLearning/Stabilizing%20Reinforcement%20Learning%20in%20Dynamic%20Environment%20with%20Application%20to%20Online%20Recommendation.pdf) - [Top-K Off-Policy Correctionfor a REINFORCE Recommender System](ReinforcementLearning/Top-K%20Off-Policy%20Correctionfor%20a%20REINFORCE%20Recommender%20System.pdf) - [Two-Stage Constrained Actor-Critic for Short Video Recommendation](ReinforcementLearning/Two-Stage%20Constrained%20Actor-Critic%20for%20Short%20Video%20Recommendation.pdf) - [Towards Capacity-Aware Broker Matching - From Recommendation to Assignment](ReinforcementLearning/Towards%20Capacity-Aware%20Broker%20Matching%20-%20From%20Recommendation%20to%20Assignment.pdf) - [Virtual-Taobao - Virtualizing Real-world Online Retail Environment for Reinforcement Learning](ReinforcementLearning/Virtual-Taobao%20-%20Virtualizing%20Real-world%20Online%20Retail%20Environment%20for%20Reinforcement%20Learning.pdf) - [When People Change their Mind - Off-Policy Evaluation in Non-stationary Recommendation Environments](ReinforcementLearning/When%20People%20Change%20their%20Mind%20-%20Off-Policy%20Evaluation%20in%20Non-stationary%20Recommendation%20Environments.pdf)