RecBole is a unified, comprehensive and efficient framework developed based on PyTorch. It aims to help the researchers to reproduce and develop recommendation models.

In the first release, our library includes 53 recommendation algorithms [Model List], covering four major categories:

  • General Recommendation

  • Sequential Recommendation

  • Context-aware Recommendation

  • Knowledge-based Recommendation

We design a unified and flexible data file format, and provide the support for 27 benchmark recommendation datasets [Collected Datasets]. A user can apply the provided script to process the original data copy, or simply download the processed datasets by our team.


  • General and extensible data structure

    We deign general and extensible data structures to unify the formatting and usage of various recommendation datasets.

  • Comprehensive benchmark models and datasets

    We implement 53 commonly used recommendation algorithms, and provide the formatted copies of 27 recommendation datasets.

  • Efficient GPU-accelerated execution

    We design many tailored strategies in the GPU environment to enhance the efficiency of our library.

  • Extensive and standard evaluation protocols

    We support a series of commonly used evaluation protocols or settings for testing and comparing recommendation algorithms.