Metadata-Version: 2.4
Name: recnexa
Version: 0.1.0
Summary: A collection of recommendation algorithms
License-Expression: GPL-3.0-only
Keywords: collaborative-filtering,factorization-machines,implicit-feedback,recommendation,ranking
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: MacOS
Requires-Python: >=3.11
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: recnexa[all]
Provides-Extra: dev
Requires-Dist: abi3audit==0.0.26; extra == "dev"
Requires-Dist: build>=1.2; extra == "dev"
Requires-Dist: packaging>=24; extra == "dev"
Requires-Dist: pytest>=8; extra == "dev"
Requires-Dist: PyYAML>=6; extra == "dev"
Requires-Dist: twine>=5; extra == "dev"
Provides-Extra: rankfm
Requires-Dist: numpy<3,>=1.23.5; extra == "rankfm"
Requires-Dist: pandas>=1.5.3; extra == "rankfm"
Provides-Extra: all
Requires-Dist: recnexa[rankfm]; extra == "all"
Dynamic: license-file

# RecNexa

RecNexa is a production-oriented collection of recommendation algorithms, designed to curate and integrate practical implementations in a consistent, maintainable way.

The project focuses on algorithm packages from the third-party community, especially valuable implementations that are no longer maintained. Where needed, it adapts and maintains them for use in modern production environments.

## Included Algorithms

- **rankfm**: A factorization machine implementation optimized for ranking. See the [rankfm documentation](docs/rankfm.md).

## Coming Soon

- **LightSANs**: A sequential recommendation algorithm implementation, currently being prepared.

## Roadmap

RecNexa will continue to collect more proven recommendation algorithms, gradually covering additional recommendation scenarios and modeling paradigms.

Installation, configuration, usage examples, and API references for each algorithm are maintained separately in the `docs` directory. This README only introduces the project and its included algorithms.

## License

This project is licensed under [GPL-3.0-only](LICENSE).
