{ "opencollection": "1.0.0", "info": { "name": "Insider One Recommendation API", "version": "1.0.0" }, "items": [ { "info": { "name": "Algorithm Descriptions", "type": "folder" }, "items": [ { "info": { "name": "Chef", "type": "http" }, "http": { "method": "GET", "url": "https://recommendation.api.useinsider.com/v2/chef", "auth": { "type": "apikey", "key": "X-REQUEST-TOKEN", "value": "{{X-REQUEST-TOKEN}}", "placement": "header" } }, "docs": "Chef is an automated recommendation algorithm that automatically identifies the best mixed strategy by testing the most popular items, top sellers, user-based, highest-discounted, new arrivals, and trending items algorithms to improve conversion rates. In a mixed strategy, you manually choose an algorithm for each slot. Chef, instead, chooses the best combination by automatically evaluating the algorithms' performance.\n\nEndpoint\n\nGET https://recommendation.api.useinsider.com/v2/chef\n\nQuery Param" }, { "info": { "name": "Complementary Products", "type": "http" }, "http": { "method": "GET", "url": "https://recommendation.api.useinsider.com/v2/complementary", "auth": { "type": "apikey", "key": "X-REQUEST-TOKEN", "value": "{{X-REQUEST-TOKEN}}", "placement": "header" } }, "docs": "The Complementary Products algorithm surfaces products that go well with the one the shopper is currently viewing, items that complete the look, set, or use case rather than replace it (e.g., a belt for trousers, a case for a phone, a lamp for a sofa).\n\nIt uses a catalog-aware map of complementary category relationships generated by a language model from your category tree, so it works even on catalogs with little or no purchase history. Performs optimally on product detail and cart pages.\n\nEndp" }, { "info": { "name": "Highest Discounted Products", "type": "http" }, "http": { "method": "GET", "url": "https://recommendation.api.useinsider.com/v2/highest-discounted", "auth": { "type": "apikey", "key": "X-REQUEST-TOKEN", "value": "{{X-REQUEST-TOKEN}}", "placement": "header" } }, "docs": "The Highest Discounted Products algorithm recommends products based on their discount ratios, prioritizing those with higher discounts. It calculates the discount ratio for each product using the original and discounted prices. Recommended products will be sorted from highest to lowest discount for the given size. For each currency type, the discount ratio is calculated separately. This type of algorithm works best on main and category pages.\n\nEndpoint\n\nGET https://recommendation.api.useinsider." }, { "info": { "name": "Purchased with Last Purchased", "type": "http" }, "http": { "method": "GET", "url": "https://recommendation.api.useinsider.com/v2/last-purchased-together", "auth": { "type": "apikey", "key": "X-REQUEST-TOKEN", "value": "{{X-REQUEST-TOKEN}}", "placement": "header" } }, "docs": "The Purchased with Last Purchased algorithm is one of Insider One's personalized algorithms. It recommends the purchased products along with the user’s last purchased product. Purchase events can be collected from Web, Mobile, and Offline (CRM) UCD events. It is available on Web Smart Recommender, API-based Recommender, and App Recommender.\n\nEndpoint\n\nGET https://recommendation.api.useinsider.com/v2/last-purchased-together\n\nQuery Parameters\n\nParameter\nSample Value\nDescription\nData Type\nRequired\n" }, { "info": { "name": "Manual Merchandising", "type": "http" }, "http": { "method": "GET", "url": "https://recommendation.api.useinsider.com/v2/manual-merchandising", "auth": { "type": "apikey", "key": "X-REQUEST-TOKEN", "value": "{{X-REQUEST-TOKEN}}", "placement": "header" } }, "docs": "The Manual Merchandising algorithm retrieves product details manually specified and returns only in-stock products. It enables you to showcase specific products or content (e.g., for special days) from a list specified in the campaign configuration. In addition, only in-stock products will be displayed in the recommendation widget. This type of algorithm can be used on all page types to promote specific products.\n\nEndpoint\n\nGET https://recommendation.api.useinsider.com/v2/manual-merchandising\n\nY" }, { "info": { "name": "Mixed Strategy", "type": "http" }, "http": { "method": "GET", "url": "https://recommendation.api.useinsider.com/v2/mixed", "auth": { "type": "apikey", "key": "X-REQUEST-TOKEN", "value": "{{X-REQUEST-TOKEN}}", "placement": "header" } }, "docs": "The Mixed Strategy algorithm creates a customized, mixed recommendation strategy that combines different recommendation types. It is a way to use different algorithms for each slot in the Recommendation Widget. The mixed strategy allows you to use multiple algorithms in a single recommendation widget on your website. It works best on the main and category pages.\n\nThe duplicated items are automatically removed by the Mixed Strategy algorithm.\n\nEndpoint\n\nGET https://recommendation.api.useinsider.c" }, { "info": { "name": "Most Popular Items", "type": "http" }, "http": { "method": "GET", "url": "https://recommendation.api.useinsider.com/v2/most-popular", "auth": { "type": "apikey", "key": "X-REQUEST-TOKEN", "value": "{{X-REQUEST-TOKEN}}", "placement": "header" } }, "docs": "The Most Popular Items algorithm recommends items by analyzing the most popular products by page views. It generates recommendations based on the page view counts during the last 30 days. The Most Popular Items algorithm works best on main, category, and product pages.\n\nEndpoint\nGET https://recommendation.api.useinsider.com/v2/most-popular\n\nQuery Parameters\n\nParameter\nSample Value\nDescription\nData Type\nRequired\n\npartnerName\nmybrand\nPartner Identifier assigned by Insider One. You can use PartnerI" }, { "info": { "name": "Most Valuable Products", "type": "http" }, "http": { "method": "GET", "url": "https://recommendation.api.useinsider.com/v2/most-valuable", "auth": { "type": "apikey", "key": "X-REQUEST-TOKEN", "value": "{{X-REQUEST-TOKEN}}", "placement": "header" } }, "docs": "The Most Valuable Products algorithm recommends items based on their contribution to total revenue. It recommends products that generate more revenue across your site. Products are recommended based on the contribution to revenue and revenue per visit. This type of algorithm can be used on all page types.\n\nEndpoint\n\nGET https://recommendation.api.useinsider.com/v2/most-valuable\n\nQuery Parameters\n\nParameter\nSample Value\nDescription\nData Type\nRequired\n\npartnerName\nmybrand\nPartner Identifier assign" }, { "info": { "name": "New Arrivals", "type": "http" }, "http": { "method": "GET", "url": "https://recommendation.api.useinsider.com/v2/new-arrivals", "auth": { "type": "apikey", "key": "X-REQUEST-TOKEN", "value": "{{X-REQUEST-TOKEN}}", "placement": "header" } }, "docs": "The New Arrivals algorithm displays products that have been newly added to the website. It directly accesses the catalog and sorts products by creation date. Changing the integration type doesn't affect the results if the product is already in the database. You can use the New Arrivals algorithm on the main page to showcase new products.\n\nFor the Publisher vertical, the updated time applies to newly released articles.\n\nEndpoint\n\nGET https://recommendation.api.useinsider.com/v2/new-arrivals\n\nQuer" }, { "info": { "name": "Purchased Together", "type": "http" }, "http": { "method": "GET", "url": "https://recommendation.api.useinsider.com/v2/purchased-together", "auth": { "type": "apikey", "key": "X-REQUEST-TOKEN", "value": "{{X-REQUEST-TOKEN}}", "placement": "header" } }, "docs": "The Purchased Together algorithm recommends items by finding complementary products purchased by other users who have also purchased the user's items. It generates recommendations based on products purchased in the same sessions and in the same locale over the past 30 days. After generating recommendations, the Purchased Together algorithm orders the results according to purchase frequency. With the Purchased Together algorithm, you can apply your users' purchase patterns to your strategies. Thi" }, { "info": { "name": "Recently Viewed Products", "type": "http" }, "http": { "method": "GET", "url": "https://recommendation.api.useinsider.com/v2/recently-viewed", "auth": { "type": "apikey", "key": "X-REQUEST-TOKEN", "value": "{{X-REQUEST-TOKEN}}", "placement": "header" } }, "docs": "The Recently Viewed Products algorithm is one of Insider One's personalized algorithms. It tracks the user’s product-view behavior collected from the UCD, capturing data from both Web and Mobile events. In return, the Recently Viewed Products algorithm provides recommendations based on the user’s recently viewed products. It performs best on the cart pages. It is available on Web Smart Recommender, API-based Recommender, and App Recommender.\n\nEndpoint\n\nGET https://recommendation.api.useinsider.c" }, { "info": { "name": "Similar Products", "type": "http" }, "http": { "method": "GET", "url": "https://recommendation.api.useinsider.com/v2/similar", "auth": { "type": "apikey", "key": "X-REQUEST-TOKEN", "value": "{{X-REQUEST-TOKEN}}", "placement": "header" } }, "docs": "The Similar Products algorithm surfaces products that share descriptive characteristics with the product a shopper is currently viewing. It compares product name, category, and textual metadata, such as material_type, tags, gender, or any custom attribute you have defined. Up to 5 additional attributes can be included and configured on the Product Attributes page.\n\nIt performs optimally on product detail and cart pages where the written description carries most of the decision weight (electronic" }, { "info": { "name": "Substitute Products", "type": "http" }, "http": { "method": "GET", "url": "https://recommendation.api.useinsider.com/v2/substitute", "auth": { "type": "apikey", "key": "X-REQUEST-TOKEN", "value": "{{X-REQUEST-TOKEN}}", "placement": "header" } }, "docs": "The Substitute Products algorithm provides users with suggestions for products that can serve as replacements for items they have interacted with. The goal is to enhance user satisfaction and engagement by presenting products that align with their functional preferences and interests. This algorithm generates recommendations based on factors such as product name similarity, view-to-view statistics, and view-to-purchase statistics.\n\nEndpoint\n\nGET https://recommendation.api.useinsider.com/v2/subst" }, { "info": { "name": "Top Sellers", "type": "http" }, "http": { "method": "GET", "url": "https://recommendation.api.useinsider.com/v2/top-sellers", "auth": { "type": "apikey", "key": "X-REQUEST-TOKEN", "value": "{{X-REQUEST-TOKEN}}", "placement": "header" } }, "docs": "The Top Sellers algorithm recommends products based on their purchase counts over the last 30 days. It works best on the main page. \n\nEndpoint\n\nGET https://recommendation.api.useinsider.com/v2/top-sellers\n\nQuery Parameters\n\nParameter\nSample Value\nDescription\nData Type\nRequired\n\npartnerName\nmybrand\nPartner Identifier assigned by Insider One. You can use PartnerID as well.\nString\nYes\n\nlocale\nus_US\nLocale of the requested product catalog\nString\nYes\n\nplatform\nweb\nRequested platform. Web comes by def" }, { "info": { "name": "Trending Products", "type": "http" }, "http": { "method": "GET", "url": "https://recommendation.api.useinsider.com/v2/trending", "auth": { "type": "apikey", "key": "X-REQUEST-TOKEN", "value": "{{X-REQUEST-TOKEN}}", "placement": "header" } }, "docs": "The Trending Products algorithm recommends items using a scoring system. It scores items based on weekly views and purchases to determine this week's trending items compared to those in the previous week. This type of algorithm works best on the main and category pages.\n\nEndpoint\n\nGET https://recommendation.api.useinsider.com/v2/trending\n\nQuery Parameters\n\nParameter\nSample Value\nDescription\nData Type\nRequired\n\npartnerName\nmybrand\nPartner Identifier assigned by Insider One. You can use PartnerID " }, { "info": { "name": "User Based", "type": "http" }, "http": { "method": "GET", "url": "https://recommendation.api.useinsider.com/v2/user-based", "auth": { "type": "apikey", "key": "X-REQUEST-TOKEN", "value": "{{X-REQUEST-TOKEN}}", "placement": "header" } }, "docs": "The User Based algorithm recommends items by finding users similar to the current user. It generates recommendations based on user behavior and product popularity.\n\nIn this algorithm, product recommendations are based on the behavior of similar users (users with close similarity index scores: viewed, purchased, or added the same or similar category products to their cart) with the current user. The algorithm recommends products that similar users have encountered in the past but that the current" }, { "info": { "name": "User Engagement", "type": "http" }, "http": { "method": "GET", "url": "https://recommendation.api.useinsider.com/v2/user-engagement", "auth": { "type": "apikey", "key": "X-REQUEST-TOKEN", "value": "{{X-REQUEST-TOKEN}}", "placement": "header" } }, "docs": "The User Engagement algorithm recommends products by analyzing the current user’s most recent interactions. It generates personalized recommendations based on real-time behaviors and evolving preferences.\n\nThe User Engagement algorithm tailors product recommendations based on the user’s most recent product-view activity, using a deep-learning transformer model that dynamically adapts to recent interactions, such as viewing or purchasing patterns. It focuses specifically on items the user has sho" }, { "info": { "name": "Viewed Together", "type": "http" }, "http": { "method": "GET", "url": "https://recommendation.api.useinsider.com/v2/viewed-together", "auth": { "type": "apikey", "key": "X-REQUEST-TOKEN", "value": "{{X-REQUEST-TOKEN}}", "placement": "header" } }, "docs": "The Viewed Together algorithm recommends items by identifying similar products users have viewed. It generates recommendations based on products viewed in the same sessions and in the same locale over the past 30 days. After generating recommendations, the Viewed Together algorithm orders the results by visit frequency (popularity). Thus, users can find complementary or substitute products for the one they are currently viewing. This increases discovery rates and the likelihood of grabbing users" }, { "info": { "name": "Visually Similar Products", "type": "http" }, "http": { "method": "GET", "url": "https://recommendation.api.useinsider.com/v2/visually-similar", "auth": { "type": "apikey", "key": "X-REQUEST-TOKEN", "value": "{{X-REQUEST-TOKEN}}", "placement": "header" } }, "docs": "The Visually Similar Products algorithm surfaces products that look like the one the shopper is currently viewing. It compares product images using a multimodal AI model, refined with light product metadata, and performs optimally on category and product detail pages in visually-driven verticals such as fashion and home décor.\n\n

Insider One processes your product catalog images to generate recommendation results.

\n\nEndpoint\nGET https://recommendation.api.useinsider.com/v2/visually-similar" } ] }, { "info": { "name": "Recommendations", "type": "folder" }, "items": [ { "info": { "name": "Get Recommendations", "type": "http" }, "http": { "method": "GET", "url": "https://recommendation.api.useinsider.com/v2/:algorithm-name", "params": [ { "name": "algorithm-name", "value": "", "type": "path" } ], "auth": { "type": "apikey", "key": "X-REQUEST-TOKEN", "value": "{{X-REQUEST-TOKEN}}", "placement": "header" } }, "docs": "The Recommendation API (SR-API) is a RESTful service for programmatically retrieving personalized product suggestions. It serves as the interface to Insider One’s recommendation engine, utilizing machine learning models, real-time user behavior analysis, and product affinity data to return structured item sets.\n\nEndpoint\nThe Recommendation API follows a standard RESTful pattern:\n\nGET https://recommendation.api.useinsider.com/v2/{algorithm-name}\n\n{algorithm-name} is the unique identifier for the " } ] } ], "bundled": true }