njuhugn / BOD

[KDD'23] Official PyTorch implementation for "Efficient Bi-Level Optimization for Recommendation Denoising".

BOD

This is the official PyTorch implementation for the paper:

Zongwei Wang, Min Gao*, Wentao Li*, Junliang Yu, Linxin Guo, Hongzhi Yin. Efficient Bi-Level Optimization for Recommendation Denoising. KDD 2023.

Requirements

numba==0.53.1
numpy==1.20.3
scipy==1.6.2
torch>=1.7.0

Usage

1. Configure the xx.conf file in the directory named conf. (xx is the name of the model you want to run)
2. Run main.py and choose the model you want to run.

Acknowledgement

The implementation is based on the open-source recommendation library SelfRec.

Please cite the following papers as the references if you use our codes.

@article{yu2022self,
  title={Self-supervised learning for recommender systems: A survey},
  author={Yu, Junliang and Yin, Hongzhi and Xia, Xin and Chen, Tong and Li, Jundong and Huang, Zi},
  journal={arXiv preprint arXiv:2203.15876},
  year={2022}
}

@inproceedings{wang2023efficient,
  author = {Zongwei Wang, Min Gao, Wentao Li, Junliang Yu, Linxin Guo, Hongzhi Yin.},
  title = {Efficient Bi-Level Optimization for Recommendation Denoising},
  booktitle = {{KDD}},
  year = {2023}
}