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 surfaces currently included in this repository are the For You Timeline and Recommended Notifications.
For You Timeline
The diagram below illustrates how major services and jobs interconnect to construct a For You Timeline.
The core components of the For You Timeline included in this repository are listed below:
Type Component Description Candidate Source search-index Find and rank In-Network posts. ~50% of posts come from this candidate source. tweet-mixer Coordination layer for fetching Out-of-Network tweet candidates from underlying compute services. user-tweet-entity-graph (UTEG) Maintains an in memory User to Post interaction graph, and finds candidates based on traversals of this graph. This is built on the GraphJet framework. Several other GraphJet based features and candidate sources are located here. follow-recommendation-service (FRS) Provides Users with recommendations for accounts to follow, and posts from those accounts. Ranking light-ranker Light Ranker model used by search index (Earlybird) to rank posts. heavy-ranker Neural network for ranking candidate posts. One of the main signals used to select timeline posts post candidate sourcing. Post mixing & filtering home-mixer Main service used to construct and serve the Home Timeline. Built on product-mixer. visibility-filters Responsible for filtering X content to support legal compliance, improve product quality, increase user trust, protect revenue through the use of hard-filtering, visible product treatments, and coarse-grained downranking. timelineranker Legacy service which provides relevance-scored posts from the Earlybird Search Index and UTEG service.
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