Twitter Recommendation Algorithm

The Twitter Recommendation Algorithm is a set of services and jobs that are responsible for constructing and serving the Home Timeline. For an introduction to how the algorithm works, please refer to our engineering blog. The diagram below illustrates how major services and jobs interconnect.

These are the main components of the Recommendation Algorithm included in this repository:

Type	Component	Description
Feature	SimClusters	Community detection and sparse embeddings into those communities.
	TwHIN	Dense knowledge graph embeddings for Users and Tweets.
	trust-and-safety-models	Models for detecting NSFW or abusive content.
	real-graph	Model to predict likelihood of a Twitter User interacting with another User.
	tweepcred	Page-Rank algorithm for calculating Twitter 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 Tweets from User B).
Candidate Source	search-index	Find and rank In-Network Tweets. ~50% of Tweets come from this candidate source.
	cr-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 Tweet 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 Tweets from those accounts.
Ranking	light-ranker	Light ranker model used by search index (Earlybird) to rank Tweets.
	heavy-ranker	Neural network for ranking candidate tweets. One of the main signals used to select timeline Tweets post candidate sourcing.
Tweet mixing & filtering	home-mixer	Main service used to construct and serve the Home Timeline. Built on product-mixer
	visibility-filters	Responsible for filtering Twitter 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 tweets from the Earlybird Search Index and UTEG service.
Software framework	navi	High performance, machine learning model serving written in Rust.
	product-mixer	Software framework for building feeds of content.
	twml	Legacy machine learning framework built on TensorFlow v1.

We include Bazel BUILD files for most components, but not a top level BUILD or WORKSPACE file.

Development environment

The unified developement environment is still in an experimental state. Contributions are welcome!

You can set up a development environment with various tooling via the nix flake:

1. Install the nix package manager and enable flakes.

2. Enter the development shell by running nix develop in the root directory of the repository.

3. Optionally, install direnv which will automatically drop you into the development environment when you cd into your local clone.

Contributing

We invite the community to submit GitHub issues and pull requests for suggestions on improving the recommendation algorithm. We are working on tools to manage these suggestions and sync changes to our internal repository. Any security concerns or issues should be routed to our official bug bounty program through HackerOne. We hope to benefit from the collective intelligence and expertise of the global community in helping us identify issues and suggest improvements, ultimately leading to a better Twitter.

Read our blog on the open source initiative here.