gorse alternatives and similar packages
Based on the "Machine Learning" category.
Alternatively, view gorse alternatives based on common mentions on social networks and blogs.
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Gorgonia
Gorgonia is a library that helps facilitate machine learning in Go. -
m2cgen
Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies -
gosseract
Go package for OCR (Optical Character Recognition), by using Tesseract C++ library -
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gago
:four_leaf_clover: Evolutionary optimization library for Go (genetic algorithm, partical swarm optimization, differential evolution) -
ocrserver
A simple OCR API server, seriously easy to be deployed by Docker, on Heroku as well -
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onnx-go
onnx-go gives the ability to import a pre-trained neural network within Go without being linked to a framework or library. -
shield
Bayesian text classifier with flexible tokenizers and storage backends for Go -
neat
Plug-and-play, parallel Go framework for NeuroEvolution of Augmenting Topologies (NEAT). -
go-featureprocessing
🔥 Fast, simple sklearn-like feature processing for Go -
neural-go
A multilayer perceptron network implemented in Go, with training via backpropagation. -
go-cluster
k-modes and k-prototypes clustering algorithms implementation in Go
Access the most powerful time series database as a service
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README
Gorse Recommender System Engine
Gorse is an open-source recommendation system written in Go. Gorse aims to be a universal open-source recommender system that can be quickly 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. Project features are as follows.
- Multi-source Recommendation: For a user, recommended items are collected from different ways (popular, latest, user-based, item-based, and collaborative filtering) and ranked by click-through rate prediction.
- AutoML: Choose the best recommendation model and strategy automatically by model searching in the background.
- Distributed Recommendation: Single node training, distributed prediction, and ability to achieve horizontal scaling in the recommendation stage.
- RESTful API: Provide RESTful APIs for data CRUD and recommendation requests.
- Dashboard: Provide dashboard for data import and export, monitoring, and cluster status checking.
Quick Start
The playground mode has been prepared for beginners. Just set up a recommender system for GitHub repositories by following commands.
- Linux/macOS:
curl -fsSL https://gorse.io/playground | bash
- Docker:
docker run -p 8088:8088 zhenghaoz/gorse-in-one --playground
The playground mode will download data from GitRec and import it into Gorse. For more information：
- Read official documents
- Visit official demo
- Discuss on Discord or GitHub Discussion
Architecture
Gorse is a single node training and distributed prediction recommender system. Gorse stores data in MySQL, MongoDB, Postgres, or ClickHouse, with intermediate results cached in Redis, MySQL, MongoDB and Postgres.
- The cluster consists of a master node, multiple worker nodes, and server nodes.
- The master node is responsible for model training, non-personalized item recommendation, configuration management, and membership management.
- The server node is responsible for exposing the RESTful APIs and online real-time recommendations.
- Worker nodes are responsible for offline recommendations for each user.
In addition, the administrator can perform system monitoring, data import and export, and system status checking via the dashboard on the master node.
Contributors
Any contribution is appreciated: report a bug, give advice or create a pull request. Read [CONTRIBUTING.md](CONTRIBUTING.md) for more information.
Acknowledgments
gorse is inspired by the following projects: