Recommendations
API for real-time personalized recommendations
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.
Solutions
Content Recommendations
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.
Examples
- Recommended Videos
- Top Picks for You
- Shoes You May Like
- You May Be Interested In
- Your Daily Articles
- Suggested for You
Recommended Accounts
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.
Examples
- People You May Know
- Channels to Explore
- Who to Follow
- Suggested for You
- Accounts You May Like
Related Accounts
Get a list of accounts who are followed by similar people and have similar interests as the account 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.
Examples
- Similar Users
- Other Accounts Like This
- Similar Channels
- Related Publishers
- Other People You Might Like
Related Content
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.
Examples
- People Who Liked Also Liked
- Customers Who Bought Also Bought
- Similar Posts
- Discover Similar Lists
- You Might Also Like
Related Topics
Get similar tags or categories based on shared qualities. For a target tag, this algorithm finds other tags from content that shares that target tag.
Examples
- Related Categories
- Discover Similar Genres
- Explore Related Topics
- Other Interests You Might Like
- Similar Tags
Sorted Content
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.
Examples
- Your Search Results
- Recommended Results
- Sort by Recommended
- Featured Items You Might Like
- Top Promotions for You
- Recommended Trending Posts
Other Solutions
