Gorse | Awesome Repository

Gorse is a personalized recommendation engine server and machine learning pipeline designed to suggest items to users based on their behavior and preferences. It operates as a distributed system that separates training, candidate generation, and serving nodes to support high-throughput workloads.

The system utilizes a multi-stage recommendation pipeline to refine results through retrieval, scoring, and reranking. It generates personalized suggestions using collaborative filtering, matrix factorization, and item-to-item similarity models, while also providing non-personalized and fallback recommendations when individual profiles are unavailable.

The infrastructure supports scaling via Kubernetes and provides a management dashboard secured with OpenID Connect. Broad capabilities include model training and evaluation, a pluggable database backend, and observability via OpenTelemetry request tracing.

Users can integrate the engine into applications through REST endpoints or dedicated SDKs for Go, Python, Rust, TypeScript, Java, and .NET.

Features

Recommendation Engines - Provides a complete system for predicting and ranking items to deliver personalized content suggestions.
Personalized Recommendation Retrieval - Generates item suggestions for specific users by leveraging collaborative filtering and item-to-item similarity logic.
Recommendation List Generators - Generates personalized item suggestions for specific users using collaborative filtering and item-to-item logic.
Collaborative Filtering Models - Uses matrix factorization to train collaborative filtering models from user-item interaction data.

Features

Recommendation Engines - Provides a complete system for predicting and ranking items to deliver personalized content suggestions.
Personalized Recommendation Retrieval - Generates item suggestions for specific users by leveraging collaborative filtering and item-to-item similarity logic.
Recommendation List Generators - Generates personalized item suggestions for specific users using collaborative filtering and item-to-item logic.
Collaborative Filtering Models - Uses matrix factorization to train collaborative filtering models from user-item interaction data.

Users can integrate the engine into applications through REST endpoints or dedicated SDKs for Go, Python, Rust, TypeScript, Java, and .NET.