At Pipfeed we take price in creating a world-class recommendation engine and have spent a lot of time perfecting it. It is hard to create a recommendation engine and even hard to measure how well the algorithm is working. Here is how Pipfeed’s recommendation engine works.
The main feed is comprised of “Plugins”. Each of these plugins “compete” to add articles to the user’s feed.
Pipfeed has a plugin system internally and each of the plugins have different approach to add articles to the user’s main feed.
- Subscriptions: Add articles from the blogs user has subscribed to
- Interests: Adds articles from user’s Interests on their profile
- Trending: Adds articles that are trending on PipFeed
- Past Liked Blogs: Adds articles from user’s previous most liked blogs
- Past Liked Interests: Adds articles from user’s past liked Interests
- Collaborative filtering: Adds articles similar to previously read articles
- Content-based filtering: Find users with similar reading behavior and then adds from that user’s past history
- And more…
The Feedback Loop
Pipfeed’s recommendation engine re-balances the importance given to each plugin based on user’s interactions. So we measure articles from which plugin is the user most interacting with and that plugin gets to put more articles in the user’s feed.
So the system keeps “rebalancing” itself over time and as the user uses the app more, the more personalized the feed becomes.