Recommend items from Popular, latest, user-based, item-based and collaborative filtering.
Search the best recommendation model automatically in the background.
Support horizontal scaling in the recommendation stage after single node training.
Expose RESTful APIs for data CRUD and recommendation requests.
Support Redis, MySQL, Postgres, MongoDB, and ClickHouse as its storage backend.
Analyze online recommendation performance from recently inserted feedback.
Provide GUI for data management, system monitoring, and cluster status checking.
The codebase is released under Apache 2 license and driven by the community.
Gorse is an open-source recommendation system written in Go. Gorse aims to be a universal open-source recommender system that can be easily introduced into a wide variety of online services. By importing items, users and interaction data into Gorse, the system will automatically train models to generate recommendations for each user.
The playground mode has been prepared for beginners. Just set up a recommender system for GitHub repositories by following the commands.
After the "Find neighbors of items" task is completed on the "Tasks" page, try to insert several feedbacks into Gorse. Suppose Bob is a frontend developer who starred several frontend repositories in GitHub. We insert his star feedback to Gorse.
Then, fetch 10 recommended items from Gorse. We can find that frontend-related repositories are recommended for Bob.
[ "mbostock:d3", "nt1m:material-framework", "mdbootstrap:vue-bootstrap-with-material-design", "justice47:f2-vue", "10clouds:cyclejs-cookie", "academicpages:academicpages.github.io", "accenture:alexia", "addyosmani:tmi", "1wheel:d3-starterkit", "acdlite:redux-promise" ]
The exact output might be different from the example since the playground dataset changes over time.