# AI use cases for Gorse (gorse-io/gorse) Repo: [gorse-io/gorse](https://github.com/gorse-io/gorse) | Use cases: 3 | Page: https://dedupedai.com/use-cases/gorse-io-gorse/ Gorse is an open-source recommender system engine written in Go. You import items, users and feedback, and it trains models and serves recommendations from latest, user-to-user, item-to-item and collaborative filtering sources through RESTful APIs, with a GUI dashboard for pipeline editing, monitoring and data management. This file holds every use case of this repo. Each one starts with a "## " heading and ends with its URL. - Spin up a working recommender in one command: https://dedupedai.com/use-cases/gorse-io-gorse/try-a-recommender-engine-in-playground-mode-with-gorse/ - Feed user behavior into recommendations over REST: https://dedupedai.com/use-cases/gorse-io-gorse/send-user-feedback-to-gorse-rest-api/ - Fetch personalized recommendations for a user: https://dedupedai.com/use-cases/gorse-io-gorse/serve-personalized-recommendations-with-gorse/ ## How to spin up a working recommender in one command with Gorse Repo: [gorse-io/gorse](https://github.com/gorse-io/gorse) | Difficulty: Beginner | Time: 20 minutes | Basis: From the README Run the playground image, which imports GitHub repository data and trains recommendations you can explore. **Who it is for:** A developer evaluating whether a self-hosted recommender fits their product. **The problem:** Setting up a recommender usually means data pipelines and model training before any demo. **The outcome:** A running Gorse instance with a dashboard at localhost:8088. ### Steps 1. Start the playground with `docker run -p 8088:8088 zhenghaoz/gorse-in-one --playground` 2. Open the dashboard at http://localhost:8088 3. Wait for the Generate item-to-item recommendation task to complete on the Tasks page 4. Browse items, users and recommendations in the dashboard ### Prompt to paste into your agent ```text Using gorse-io/gorse, run `docker run -p 8088:8088 zhenghaoz/gorse-in-one --playground`, wait for the item-to-item task to finish, and describe what the dashboard shows. ``` ### Caveat Playground mode downloads data from GitRec on startup, so it needs internet access. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/gorse-io-gorse/try-a-recommender-engine-in-playground-mode-with-gorse/ ## How to feed user behavior into recommendations over REST with Gorse Repo: [gorse-io/gorse](https://github.com/gorse-io/gorse) | Difficulty: Beginner | Time: 20 minutes | Basis: From the README Post star or click feedback for a user and watch it change what Gorse recommends. **Who it is for:** A backend developer wiring product events into a recommender. **The problem:** Recommendations are only as good as the user signals you can feed in. **The outcome:** Feedback records stored in Gorse for a user, ready for training. ### Steps 1. Start Gorse, for example with the playground image 2. Build a JSON array of feedback objects with FeedbackType, UserId, ItemId, Value and Timestamp 3. Send it with `curl -X POST http://127.0.0.1:8088/api/feedback` using the Content-Type header and -d payload shown in the README 4. Check the stored feedback in the dashboard ### Prompt to paste into your agent ```text Using gorse-io/gorse, post five star feedback events for user to the /api/feedback endpoint as the README shows, then confirm they appear in the dashboard. ``` ### Caveat Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/gorse-io-gorse/send-user-feedback-to-gorse-rest-api/ ## How to fetch personalized recommendations for a user with Gorse Repo: [gorse-io/gorse](https://github.com/gorse-io/gorse) | Difficulty: Intermediate | Time: 30 minutes | Basis: From the README Request the top items for a user from the recommend endpoint and plug them into your app. **Who it is for:** A product engineer adding a recommended for you list. **The problem:** Teams need a simple API that returns ranked items per user. **The outcome:** A list of recommended item IDs for a user that reflects their feedback. ### Steps 1. Make sure the user has feedback in Gorse 2. Call `curl http://127.0.0.1:8088/api/recommend/bob?n=10` with your user ID in place of bob 3. Render the returned items in your app 4. For scale, split into master, worker and server nodes as the Architecture section describes ### Prompt to paste into your agent ```text Using gorse-io/gorse, call `curl http://127.0.0.1:8088/api/recommend/bob?n=10` for before and after posting new feedback, and show how the list changed. ``` ### Caveat Gorse trains on a single node and predicts in a distributed way; production storage needs MySQL, MongoDB or Postgres. Risk, network-exposure: The quick start exposes the API on port 8088 with no authentication step shown; keep it off public networks. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/gorse-io-gorse/serve-personalized-recommendations-with-gorse/