# Gorse — Open-Source Recommendation System in Go

> Gorse is a self-hosted recommendation engine written in Go that delivers real-time personalized content, product, and item suggestions through a simple REST API with no machine learning expertise required.

## Install

Save as a script file and run:

# Gorse — Open-Source Recommendation System in Go

## Quick Use
```bash
docker compose -f https://raw.githubusercontent.com/gorse-io/gorse/release-0.4/docker-compose.yml up -d
# Dashboard: http://localhost:8088 — API: http://localhost:8087
curl http://localhost:8087/api/recommend/user1?n=10
```

## Introduction
Gorse is an open-source recommendation system written in Go that automates collaborative filtering and content-based recommendations. It provides a REST API that returns personalized item suggestions without requiring teams to build their own ML pipelines or understand recommendation algorithms in depth.

## What Gorse Does
- Generates personalized item recommendations for each user based on interaction history
- Supports collaborative filtering, content-based filtering, and hybrid approaches
- Provides a RESTful API for inserting feedback, users, items, and fetching recommendations
- Includes a web dashboard for monitoring recommendation quality and system health
- Auto-tunes model hyperparameters using random search to optimize recommendation accuracy

## Architecture Overview
Gorse follows a master-worker architecture. The master node coordinates model training, data preprocessing, and API serving, while worker nodes handle the compute-intensive tasks of model fitting and recommendation generation. Data is stored in MySQL, PostgreSQL, or MongoDB for persistent state, with Redis or the built-in cache for low-latency recommendation serving. The system periodically retrains models in the background to incorporate new user feedback.

## Self-Hosting & Configuration
- Deploy via Docker Compose with Redis and a SQL database (MySQL or PostgreSQL)
- Configure through a TOML config file specifying database URLs, model parameters, and cache TTL
- Scale horizontally by adding worker nodes for larger item catalogs and user bases
- Set recommendation refresh intervals based on how frequently your content changes
- Environment variables override config file values for container-based deployments

## Key Features
- Zero ML expertise required: insert users, items, and feedback; Gorse handles model selection
- Auto hyperparameter tuning selects the best algorithm configuration automatically
- Multi-source recommendation blending: latest items, popular items, collaborative, and content-based
- Distributed architecture scales from a single Docker container to a multi-node cluster
- RESTful API with Swagger documentation for straightforward integration

## Comparison with Similar Tools
- **LensKit** — Python library for offline recommendation research; not a deployable service
- **Surprise** — Python scikit for building and evaluating recommender systems; research-oriented
- **Recbole** — PyTorch-based framework for benchmarking; heavier and more research-focused
- **Amazon Personalize** — Managed AWS service; no self-hosting and usage-based pricing
- **LightFM** — Python library for hybrid recommendations; must be wrapped in a custom API

## FAQ
**Q: What types of feedback does Gorse support?**
A: Gorse accepts both explicit feedback (ratings) and implicit feedback (clicks, views, purchases). Implicit feedback is more common in production settings.

**Q: How many items and users can Gorse handle?**
A: Gorse has been tested with millions of items and users. Performance depends on the number of worker nodes and the backing database capacity.

**Q: Does Gorse support real-time updates?**
A: Yes. New feedback is ingested immediately via the API and influences the next recommendation refresh cycle. The cache TTL controls how quickly recommendations update.

**Q: Can I use Gorse for content other than products?**
A: Yes. Gorse is domain-agnostic. It works for articles, videos, music, jobs, or any item type as long as you provide user-item interaction data.

## Sources
- https://github.com/gorse-io/gorse
- https://gorse.io/docs/

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Source: https://tokrepo.com/en/workflows/b5450c9e-3f0f-11f1-9bc6-00163e2b0d79
Author: Script Depot