RecFM

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Comprehensive tools and frameworks for developing foundation models tailored to recommendation systems.

RecFM 최신버젼 다운로드

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# RecFM

Comprehensive tools, projects and frameworks for developing foundation models tailored to recommendation systems, developped by the USTCLLM group at USTC, leaded by [Prof. Defu Lian](https://faculty.ustc.edu.cn/liandefu).

## Overview

RecFM is a collection of tools and frameworks designed to facilitate the development of robust and efficient recommendation systems. It includes a suite of modular libraries and technologies that cater to various aspects of recommendation system development, from data ingestion to model deployment.

## Projects

1. **RecStudio**: A modular recommendation system algorithm library that enables the rapid construction and efficient training of recommendation system models. It also features a visualization platform for better understanding and debugging of models.

2. **RecStudio4Industry**: The industrial version of RecStudio, designed for high-efficiency model construction and training. It supports data reading from industry-friendly data interfaces such as HDFS and offers quick model deployment and inference capabilities, ideal for building multi-stage recommendation service frameworks.

3. **CELA**: A cost-effective text embedding model alignment technology for recommendation systems. It converts any text embedding model into a text encoding model that is friendly to recommendation models.

4. **GRE**: A universal recommendation text embedding model that has been deeply trained and aligned on multiple public datasets across different recommendation domains. It serves multi-domain recommendation systems for text representation extraction.

5. **Nexus**: Nexus is the first Pytorch-based information retrieval development toolkit aimed at industrial internet applications such as recommendation system and document retrieval.

## Usage

For detailed usage instructions, please refer to the documentation in each subdir.

## Contributing

If you'd like to contribute to RecFM, please follow the [contribution guidelines](LINK_TO_CONTRIBUTION_GUIDELINES).

## License

Apache 2.0

## Contact

For any questions or inquiries, you can reach out to us at liandefu@ustc.edu.cn.

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