The Pipeless Recommendations API is an efficient method to add recommended content to your website or app. Whether you want to recommend e-commerce products or articles or any type of media, the Recommendations API can supply you with personalized content in real-time. Our system supports any kind of action users might have taken on content or other accounts, whether that action is purchased, shared, read, followed, liked, watched, or other common behaviors could be used to inform recommendations.
Read through the What's Next links below for descriptions and examples for each of the Recommendation API endpoints. Or you can go right to the API reference docs here.
Get a personalized set of recommended content based on a user’s interests and what similar users have engaged with. This algorithm utilizes collaborative filtering based on selected engagement signals and categories/tags for a wide variety of recommendation formats.
Get a personalized list of accounts to follow based on a user’s interests and who similar users follow. This algorithm combines collaborative filtering of similar following behavior with tags from content that a user has engaged with positively in order to provide recommendations of other accounts.
Get similar tags or categories based on shared qualities. This algorithm uses collaborative filtering to find other tags that have been similarly engaged with by other users.
Get content similar to other content based on categories or tags along with user engagement. For a target object, this algorithm uses associated categories/tags along with collaborative filtering from selected user engagement signals to find similar content.
Get a list of accounts who are followed by similar people and have similar interests as the user being viewed. For a target account, this algorithm combines collaborative filtering of similar following behavior with tags from that account's content to find similar accounts.
Get a personalized sorting of a pre-defined set of content based on a user’s interests and what similar users have engaged with. This algorithm utilizes collaborative filtering based on selected engagement signals and categories/tags.
Updated over 1 year ago