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HomePage | Docs | GitHub | Datasets | v0.1.2 | v0.2.0 | v1.0.0

Introduction

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 81 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 28 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.

asset/framework.png

Features:

  • 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 81 commonly used recommendation algorithms, and provide the formatted copies of 28 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.
.. toctree::
   :maxdepth: 1
   :caption: Get Started

   get_started/install
   get_started/quick_start
   get_started/distributed_training


.. toctree::
   :maxdepth: 1
   :caption: User Guide

   user_guide/config_settings
   user_guide/data_intro
   user_guide/model_intro
   user_guide/train_eval_intro
   user_guide/usage



.. toctree::
   :maxdepth: 1
   :caption: Developer Guide

   developer_guide/customize_models
   developer_guide/customize_trainers
   developer_guide/customize_dataloaders
   developer_guide/customize_samplers
   developer_guide/customize_metrics



.. toctree::
   :maxdepth: 1
   :caption: API REFERENCE:

   recbole/recbole.config.configurator
   recbole/recbole.data
   recbole/recbole.evaluator
   recbole/recbole.model
   recbole/recbole.quick_start.quick_start
   recbole/recbole.sampler.sampler
   recbole/recbole.trainer.hyper_tuning
   recbole/recbole.trainer.trainer
   recbole/recbole.utils.case_study
   recbole/recbole.utils.enum_type
   recbole/recbole.utils.logger
   recbole/recbole.utils.utils

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.

Time Version Lead Developers
June 2020 ~ Nov. 2020 v0.1.1 Shanlei Mu, Yupeng Hou, Zihan Lin, Kaiyuan Li
Nov. 2020 ~ Now v0.1.2 ~ v1.0.1 Yushuo Chen, Xingyu Pan

License

RecBole uses MIT License.