364-disentangled-contrastive-learning-for-social-recommendation

https://github.com/szu-advtech-2023/364-disentangled-contrastive-learning-for-social-recommendation

Science Score: 41.0%

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Basic Info

Host: GitHub
Owner: SZU-AdvTech-2023
Language: Python
Default Branch: main
Size: 11.6 MB

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Created over 2 years ago · Last pushed over 2 years ago

Metadata Files

Citation

https://github.com/SZU-AdvTech-2023/364-Disentangled-Contrastive-Learning-for-Social-Recommendation/blob/main/

# Introduction
In this work, 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](https://dl.acm.org/doi/abs/10.1145/3511808.3557583)
```
@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}
}

Owner

Name: SZU-AdvTech-2023
Login: SZU-AdvTech-2023
Kind: organization

Repositories: 1
Profile: https://github.com/SZU-AdvTech-2023

Citation (citation.txt)

@article{REPO364,
    author = "Wu, Jiahao and Fan, Wenqi and Chen, Jingfan and Liu, Shengcai and Li, Qing and Tang, Ke Tang",
    booktitle = "CIKM '22: Proceedings of the 31st ACM International Conference on Information \& Knowledge Management",
    title = "{Disentangled Contrastive Learning for Social Recommendation}",
    year = "2022"
}

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