RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms

@article{Zhao2021RecBoleTA,
  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 Haibo 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={Proceedings of the 30th ACM International Conference on Information \& Knowledge Management},
  year={2021}
}
  • Wayne Xin Zhao, Shanlei Mu, +16 authors Ji-Rong Wen
  • Published 3 November 2020
  • Computer Science
  • Proceedings of the 30th ACM International Conference on Information & Knowledge Management
In recent years, there are a large number of recommendation algorithms proposed in the literature, from traditional collaborative filtering to deep learning algorithms. However, the concerns about how to standardize open source implementation of recommendation algorithms continually increase in the research community. In the light of this challenge, we propose a unified, comprehensive and efficient recommender system library called RecBole (pronounced as [rEk'boUl@r]), which provides a unified鈥β

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