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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 lastest release, our library includes 91 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 43 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 91 commonly used recommendation algorithms, and provide the formatted copies of 43 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.

The Team

RecBole is developed and maintained by RUC, BUPT, ECNU.

Here is the list of our lead developers in each development phase. They are the souls of RecBole and have made outstanding contributions.



Lead Developers

June 2020 ~ Nov. 2020


Shanlei Mu, Yupeng Hou, Zihan Lin, Kaiyuan Li

Nov. 2020 ~ Oct. 2022

v0.1.2 ~ v1.0.1

Yushuo Chen, Xingyu Pan

Oct. 2022 ~ Nov. 2023

v1.1.0 ~ v1.1.1

Lanling Xu, Zhen Tian, Gaowei Zhang, Lei Wang, Junjie Zhang

Nov. 2023 ~ now


Bowen Zheng, Chen Ma


RecBole uses MIT License.