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Expand Up @@ -6,61 +6,61 @@ X's Recommendation Algorithm is a set of services and jobs that are responsible

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 service that handles the reading and writing of post data. |
| | [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 Posts. |
| | [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 an 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 posts from User B). |
| | [topic-social-proof](topic-social-proof/README.md) | Identifies topics related to individual posts. |
| | [representation-scorer](representation-scorer/README.md) | Compute scores between pairs of entities (Users, Posts, etc.) using embedding similarity. |
| Software framework | [navi](navi/README.md) | High performance, machine learning model serving written in Rust. |
| | [product-mixer](product-mixer/README.md) | Software framework for building feeds of content. |
| | [timelines-aggregation-framework](timelines/data_processing/ml_util/aggregation_framework/README.md) | Framework for generating aggregate features in batch or real time. |
| | [representation-manager](representation-manager/README.md) | Service to retrieve embeddings (i.e. SimClusers and TwHIN). |
| | [twml](twml/README.md) | Legacy machine learning framework built on TensorFlow v1. |
| Type | Component | Description |
|--------------------|--------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------|
| Data | [`tweetypie`](tweetypie/server/README.md) | Core service that handles the reading and writing of post data. |
| | [`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 Posts. |
| | [`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 an 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 posts from User B). |
| | [`topic-social-proof`](topic-social-proof/README.md) | Identifies topics related to individual posts. |
| | [`representation-scorer`](representation-scorer/README.md) | Compute scores between pairs of entities (Users, Posts, etc.) using embedding similarity. |
| Software framework | [`navi`](navi/README.md) | High performance, machine learning model serving written in Rust. |
| | [`product-mixer`](product-mixer/README.md) | Software framework for building feeds of content. |
| | [`timelines-aggregation-framework`](timelines/data_processing/ml_util/aggregation_framework/README.md) | Framework for generating aggregate features in batch or real time. |
| | [`representation-manager`](representation-manager/README.md) | Service to retrieve embeddings (i.e. SimClusers and TwHIN). |
| | [`twml`](twml/README.md) | 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.

![](docs/system-diagram.png)
![System Diagram](docs/system-diagram.png)

The core components of the For You Timeline included in this repository are listed below:

| Type | Component | Description |
|------------|------------|------------|
| Candidate Source | [search-index](src/java/com/twitter/search/README.md) | Find and rank In-Network posts. ~50% of posts come from this candidate source. |
| | [tweet-mixer](tweet-mixer) | Coordination layer for fetching Out-of-Network tweet candidates from underlying compute services. |
| | [user-tweet-entity-graph](src/scala/com/twitter/recos/user_tweet_entity_graph/README.md) (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](https://github.com/twitter/GraphJet) framework. Several other GraphJet based features and candidate sources are located [here](src/scala/com/twitter/recos). |
| | [follow-recommendation-service](follow-recommendations-service/README.md) (FRS)| Provides Users with recommendations for accounts to follow, and posts from those accounts. |
| Ranking | [light-ranker](src/python/twitter/deepbird/projects/timelines/scripts/models/earlybird/README.md) | Light Ranker model used by search index (Earlybird) to rank posts. |
| | [heavy-ranker](https://github.com/twitter/the-algorithm-ml/blob/main/projects/home/recap/README.md) | Neural network for ranking candidate posts. One of the main signals used to select timeline posts post candidate sourcing. |
| Post mixing & filtering | [home-mixer](home-mixer/README.md) | Main service used to construct and serve the Home Timeline. Built on [product-mixer](product-mixer/README.md). |
| | [visibility-filters](visibilitylib/README.md) | 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](timelineranker/README.md) | Legacy service which provides relevance-scored posts from the Earlybird Search Index and UTEG service. |
| Type | Component | Description |
|-------------------------|-------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Candidate Source | [`search-index`](src/java/com/twitter/search/README.md) | Find and rank In-Network posts. ≈50% of posts come from this candidate source. |
| | [`tweet-mixer`](tweet-mixer) | Coordination layer for fetching Out-of-Network tweet candidates from underlying compute services. |
| | [`user-tweet-entity-graph`](src/scala/com/twitter/recos/user_tweet_entity_graph/README.md) (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](https://github.com/twitter/GraphJet) framework. Several other GraphJet based features and candidate sources are located [here](src/scala/com/twitter/recos). |
| | [`follow-recommendation-service`](follow-recommendations-service/README.md) (FRS) | Provides Users with recommendations for accounts to follow, and posts from those accounts. |
| Ranking | [`light-ranker`](src/python/twitter/deepbird/projects/timelines/scripts/models/earlybird/README.md) | Light Ranker model used by search index (Earlybird) to rank posts. |
| | [`heavy-ranker`](https://github.com/twitter/the-algorithm-ml/blob/main/projects/home/recap/README.md) | Neural network for ranking candidate posts. One of the main signals used to select timeline posts post candidate sourcing. |
| Post mixing & filtering | [`home-mixer`](home-mixer/README.md) | Main service used to construct and serve the Home Timeline. Built on [`product-mixer`](product-mixer/README.md). |
| | [`visibility-filters`](visibilitylib/README.md) | 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`](timelineranker/README.md) | Legacy service which provides relevance-scored posts from the Earlybird Search Index and UTEG service. |

### Recommended Notifications

The core components of Recommended Notifications included in this repository are listed below:

| Type | Component | Description |
|------------|------------|------------|
| 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 posts. 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. |
| Type | Component | Description |
|---------|------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 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 posts. 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. |

## Build and test code

We include Bazel BUILD files for most components, but not a top-level BUILD or WORKSPACE file. We plan to add a more complete build and test system in the future.
We include Bazel `BUILD` files for most components, but not a top-level `BUILD` or `WORKSPACE` file. We plan to add a more complete build and test system in the future.

## Contributing

Expand Down