He might share articles he finds interesting, watch videos from his favorite game streamers, or leave thoughtful comments on posts from friends. In this case, our ranking algorithm would rank Saanvi’s running video higher than Wei’s cocker spaniel photo because it predicts a higher probability that Juan will like that piece of content.īut is liking the only way Juan expresses his preferences? Surely not. On the other hand, perhaps Juan has previously engaged more with video content than photos, so the like prediction for Wei’s cocker spaniel photo might be lower.
C ) such as the type of post or the relationship between the viewer and the author of the post (e.g., whether they marked each other as family members) and the function f(.) combines the attributes into a single value.įor example, if Juan tends to interact with Saanvi a lot or share the content Saanvi posts, and the running video is very recent (e.g., from this morning), we might see a high probability that Juan likes content like this. Mathematically, for each post i, we estimate Y ijt = f(x ijt1 x ijt2 … x ijtC ), where c represents a characteristic c (1. Given various attributes we know about a post (who is tagged in a photo, when it was posted, etc.), we can use the characteristics of the post X it toward viewer j at time t, and predict Y ijt (whether Juan might like the post). On Facebook, one concrete observable signal that an item has value for someone is if they click the like button. Take Saanvi’s running video, for example.
In mathematical terms, we need to define an objective function for Juan and perform a single-objective optimization. To rank some of these things higher than others in Juan’s News Feed, we need to learn what matters most to Juan and which content carries the highest value for him. And his favorite Page published an interesting article about the best way to view the Milky Way at night, while his favorite cooking Group posted four new sourdough recipes.īecause Juan is connected to or has chosen to follow the producers of this content, it’s all likely to be relevant or interesting to him. Another friend, Saanvi, posted a video from her morning run. Since Juan’s login yesterday, his good friend Wei posted a photo of his cocker spaniel. To understand how this works, let’s start with a hypothetical person logging in to Facebook: We’ll call him Juan.
We are sharing new details of how we designed an ML-powered News Feed ranking system.
CLOUDTV POWERED FB OFFLINE
Models for meaningful interactions and quality content are powered by state-of-the-art ML, such as multitask learning on neural networks, embeddings, and offline learning systems. Ranking exists to help solve these problems, but how can you build a system that presents so many different types of content in a way that’s personally relevant to billions of people around the world? We use ML to predict which content will matter most to each person to support a more engaging and positive experience. Without machine learning (ML), people’s News Feeds could be flooded with content they don’t find as relevant or interesting, including overly promotional content or content from acquaintances who post frequently, which can bury the content from the people they’re closest to. This is something we tackle every day with News Feed ranking. Designing a personalized ranking system for more than 2 billion people (all with different interests) and a plethora of content to select from presents significant, complex challenges.
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