Source code for the X Recommendation Algorithm

Summary

X's Recommendation Algorithm X's Recommendation Algorithm is a set of services and jobs that are responsible for serving feeds of posts and other content across all X product surfaces (e.g. For You Timeline, Search, Explore, Notifications). For an introduction to how the algorithm works, please refer to our engineering blog. Architecture Product surfaces at X are built on a shared set of data, models, and software frameworks. The shared components included in this repository are listed below: Type Component Description Data tweetypie Core service that handles the reading and writing of post data. unified-user-actions Real-time stream of user actions on X. user-signal-service Centralized platform to retrieve explicit (e.g. likes, replies) and implicit (e.g. profile visits, tweet clicks) user signals. Model SimClusters Community detection and sparse embeddings into those communities. TwHIN Dense knowledge graph embeddings for Users and Posts. trust-and-safety-models Models for detecting NSFW or abusive content. real-graph Model to predict the likelihood of an X User interacting with another User. tweepcred Page-Rank algorithm for calculating X User reputation. recos-injector Streaming event processor for building input streams for GraphJet based services. graph-feature-service Serves graph features for a directed pair of users (e.g. how many of User A's following liked posts from User B). topic-social-proof Identifies topics related to individual posts. representation-scorer Compute scores between pairs of entities (Users, Posts, etc.) using embedding similarity. Software framework navi High performance, machine learning model serving written in Rust. product-mixer Software framework for building feeds of content. timelines-aggregation-framework Framework for generating aggregate features in batch or real time. representation-manager Service to retrieve embeddings (i.e. SimClusers and TwHIN). twml Legacy machine learning framework built on TensorFlow v1. The product s...