Explore Gorse: An Open‑Source Go Recommendation Engine for Scalable AI‑Driven Suggestions

Today we introduce Gorse, an open‑source recommendation system written in Go.

Gorse aims to be a universal recommendation engine that can be quickly integrated into various online services. By importing items, users, and interaction data, the system automatically trains models to generate personalized recommendations.

Automation: backend model search automatically selects the best recommendation model and strategy.

Distributed recommendation: single‑node training, distributed prediction, and horizontal scaling during the recommendation phase.

RESTful API: provides CRUD operations for data and endpoints for recommendation requests.

Dashboard: offers data import/export, monitoring, and cluster status checks.

Gorse stores data in MySQL or MongoDB, caches intermediate data in Redis, and consists of a master node, multiple worker nodes, and server nodes. The master handles model training, non‑personalized item recommendation, configuration, and user management. Server nodes expose the RESTful API and real‑time recommendations, while workers generate offline recommendations for each user.

The main workflow is:

User feedback is collected into the data store.

Archived feedback is extracted to train ranking and CTR models.

Offline recommendations are generated and cached in the background.

Online recommendations are served in real time from the cached offline results.

Gorse has attracted 3.8K stars and 274 forks on GitHub.

GitHub repository: https://github.com/zhenghaoz/gorse