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# Twitter's Recommendation Algorithm
# x Recommendation Algorithm
Twitter's Recommendation Algorithm is a set of services and jobs that are responsible for serving feeds of Tweets and other content across all Twitter product surfaces (e.g. For You Timeline, Search, Explore, Notifications). For an introduction to how the algorithm works, please refer to our [engineering blog](https://blog.twitter.com/engineering/en_us/topics/open-source/2023/twitter-recommendation-algorithm).
x's Recommendation Algorithm is a set of services and jobs that are responsible for serving feeds of Tweets and other content across all Twitter product surfaces (e.g. For You Timeline, Search, Explore, Notifications). For an introduction to how the algorithm works, please refer to our [engineering blog](https://blog.twitter.com/engineering/en_us/topics/open-source/2023/twitter-recommendation-algorithm).
## Architecture
Product surfaces at Twitter are built on a shared set of data, models, and software frameworks. The shared components included in this repository are listed below:
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](tweetypie/server/README.md) | Core Tweet service that handles the reading and writing of Tweet data. |
| | [unified-user-actions](unified_user_actions/README.md) | Real-time stream of user actions on Twitter. |
| | [unified-user-actions](unified_user_actions/README.md) | Real-time stream of user actions on x. |
| | [user-signal-service](user-signal-service/README.md) | Centralized platform to retrieve explicit (e.g. likes, replies) and implicit (e.g. profile visits, tweet clicks) user signals. |
| Model | [SimClusters](src/scala/com/twitter/simclusters_v2/README.md) | Community detection and sparse embeddings into those communities. |
| | [TwHIN](https://github.com/twitter/the-algorithm-ml/blob/main/projects/twhin/README.md) | Dense knowledge graph embeddings for Users and Tweets. |
| | [trust-and-safety-models](trust_and_safety_models/README.md) | Models for detecting NSFW or abusive content. |
| | [real-graph](src/scala/com/twitter/interaction_graph/README.md) | Model to predict the likelihood of a Twitter User interacting with another User. |
| | [tweepcred](src/scala/com/twitter/graph/batch/job/tweepcred/README) | Page-Rank algorithm for calculating Twitter User reputation. |
| | [real-graph](src/scala/com/twitter/interaction_graph/README.md) | Model to predict the likelihood of a x User interacting with another User. |
| | [tweepcred](src/scala/com/twitter/graph/batch/job/tweepcred/README) | Page-Rank algorithm for calculating x User reputation. |
| | [recos-injector](recos-injector/README.md) | Streaming event processor for building input streams for [GraphJet](https://github.com/twitter/GraphJet) based services. |
| | [graph-feature-service](graph-feature-service/README.md) | Serves graph features for a directed pair of Users (e.g. how many of User A's following liked Tweets from User B). |
| | [topic-social-proof](topic-social-proof/README.md) | Identifies topics related to individual Tweets. |
@ -54,7 +54,7 @@ The core components of Recommended Notifications included in this repository are
| Type | Component | Description |
|------------|------------|------------|
| Service | [pushservice](pushservice/README.md) | Main recommendation service at Twitter used to surface recommendations to our users via notifications.
| Service | [pushservice](pushservice/README.md) | Main recommendation service at X used to surface recommendations to our users via notifications.
| Ranking | [pushservice-light-ranker](pushservice/src/main/python/models/light_ranking/README.md) | Light Ranker model used by pushservice to rank Tweets. Bridges candidate generation and heavy ranking by pre-selecting highly-relevant candidates from the initial huge candidate pool. |
| | [pushservice-heavy-ranker](pushservice/src/main/python/models/heavy_ranking/README.md) | Multi-task learning model to predict the probabilities that the target users will open and engage with the sent notifications. |
@ -64,6 +64,6 @@ We include Bazel BUILD files for most components, but not a top-level BUILD or W
## 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](https://hackerone.com/twitter) 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.
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](https://hackerone.com/twitter) 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 X.
Read our blog on the open source initiative [here](https://blog.twitter.com/en_us/topics/company/2023/a-new-era-of-transparency-for-twitter).