austingg / RecBole

A unified, comprehensive and efficient recommendation library

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RecBole (伯乐)


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RecBole is developed based on Python and PyTorch for reproducing and developing recommendation algorithms in a unified, comprehensive and efficient framework for research purpose. Our library includes 53 recommendation algorithms, 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. A user can apply the provided script to process the original data copy, or simply download the processed datasets by our team.

RecBole v0.1 architecture
Figure: RecBole Overall Architecture


  • General and extensible data structure. We design 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 optimize the efficiency of our library with a number of improved techniques oriented to the GPU environment.

  • Extensive and standard evaluation protocols. We support a series of widely adopted evaluation protocols or settings for testing and comparing recommendation algorithms.

RecBole News

11/03/2020: We release the first version of RecBole v0.1.1.


RecBole works with the following operating systems:

  • Linux
  • Windows 10
  • macOS X

RecBole requires Python version 3.6 or later.

RecBole requires torch version 1.6.0 or later. If you want to use RecBole with GPU, please ensure that CUDA or cudatoolkit version is 9.2 or later. This requires NVIDIA driver version >= 396.26 (for Linux) or >= 397.44 (for Windows10).

Install from conda

conda install -c aibox recbole

Install from pip

pip install recbole

Install from source

git clone && cd RecBole
pip install -e . --verbose


With the source code, you can use the provided script for initial usage of our library:


This script will run the BPR model on the ml-100k dataset.

Typically, this example takes less than one minute. We will obtain some output like:

INFO ml-100k
The number of users: 944
Average actions of users: 106.04453870625663
The number of items: 1683
Average actions of items: 59.45303210463734
The number of inters: 100000
The sparsity of the dataset: 93.70575143257098%

INFO Evaluation Settings:
Group by user_id
Ordering: {'strategy': 'shuffle'}
Splitting: {'strategy': 'by_ratio', 'ratios': [0.8, 0.1, 0.1]}
Negative Sampling: {'strategy': 'full', 'distribution': 'uniform'}

    (user_embedding): Embedding(944, 64)
    (item_embedding): Embedding(1683, 64)
    (loss): BPRLoss()
Trainable parameters: 168128

INFO epoch 0 training [time: 0.27s, train loss: 27.7231]
INFO epoch 0 evaluating [time: 0.12s, valid_score: 0.021900]
INFO valid result:
recall@10: 0.0073  mrr@10: 0.0219  ndcg@10: 0.0093  hit@10: 0.0795  precision@10: 0.0088


INFO epoch 63 training [time: 0.19s, train loss: 4.7660]
INFO epoch 63 evaluating [time: 0.08s, valid_score: 0.394500]
INFO valid result:
recall@10: 0.2156  mrr@10: 0.3945  ndcg@10: 0.2332  hit@10: 0.7593  precision@10: 0.1591

INFO Finished training, best eval result in epoch 52
INFO Loading model structure and parameters from saved/***.pth
INFO best valid result:
recall@10: 0.2169  mrr@10: 0.4005  ndcg@10: 0.235  hit@10: 0.7582  precision@10: 0.1598
INFO test result:
recall@10: 0.2368  mrr@10: 0.4519  ndcg@10: 0.2768  hit@10: 0.7614  precision@10: 0.1901

If you want to change the parameters, such as learning_rate, embedding_size, just set the additional command parameters as you need:

python --learning_rate=0.0001 --embedding_size=128

If you want to change the models, just run the script by setting additional command parameters:

python --model=[model_name]

RecBole Major Releases

Releases Date Features
v0.1.1 11/03/2020 Basic RecBole


Please let us know if you encounter a bug or have any suggestions by filing an issue.

We welcome all contributions from bug fixes to new features and extensions.

We expect all contributions discussed in the issue tracker and going through PRs.


If you find RecBole useful for your research or development, please cite the following paper:

    title={RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms},
    author={Wayne Xin Zhao and Shanlei Mu and Yupeng Hou and Zihan Lin and Kaiyuan Li and Yushuo Chen and Yujie Lu and Hui Wang and Changxin Tian and Xingyu Pan and Yingqian Min and Zhichao Feng and Xinyan Fan and Xu Chen and Pengfei Wang and Wendi Ji and Yaliang Li and Xiaoling Wang and Ji-Rong Wen},
    journal={arXiv preprint arXiv:2011.01731}

The Team

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


RecBole uses MIT License.

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A unified, comprehensive and efficient recommendation library

License:MIT License


Language:Python 100.0%Language:Shell 0.0%