How machine learning powers Facebook’s News Feed ranking algorithm

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This post details the machine learning-powered ranking algorithm for Facebook's News Feed. It explains how ML is used to predict user engagement (e.g., likes, shares, comments) for individual content items by analyzing various features of the post and the user. The system uses multitask neural networks and embeddings to score candidate posts and combines multiple predictions into a single ranking score (V_ijt). It also describes the multi-pass scoring system, including a lightweight pass for initial filtering, a main scoring pass for individual post evaluation, and a contextual pass for diversity rules. The post highlights the importance of aligning ranking objectives with user-perceived meaningfulness through surveys and research into personalization based on observational data.