DcRec-Pytorch

This is the pytorch implementation of our CIKM 2022 short paper:

CIKM 2022. Jiahao Wu, Wenqi Fan, Jingfan Chen, Shengcai Liu, Qing Li, Ke Tang. Disentangled Contrastive Learning for Social Recommendation. Paper in arXiv.

Introduction

In this work, we propose a novel disentangled contrastive learning framework for social Recommendations to model heterogeneous behavior patterns of users in item domain and social domain.

Environment

pip install -r requirements.txt

Dataset

We conduct experiments on two datasets: Ciao and Douban.

Command

cd code && python main.py

NOTE:

1. The setting of hyperparameters could be found in the file of code/main.py.
2. The optimal hyperparameters could refer to the logs in fold of logs of optimal hyperparameters.
3. If you find our work helpful, please cite: Disentangled Contrastive Learning for Social Recommendation

@inproceedings{wu2022DcRec,
  author = {Wu, Jiahao and Fan, Wenqi and Chen, Jingfan and Liu, Shengcai and Li, Qing and Tang, Ke},
  title = {Disentangled Contrastive Learning for Social Recommendation},
  year = {2022},
  publisher = {ACM},
  booktitle = {Proc. of CIKM'2022}
}

Acknowledgements

Thanks to the authors of LightGCN since our implementation is partially based on their pytorch implementation.