DR-GNN

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[WWW2024] The official code for paper "Distributionally Robust Graph-based Recommendation System"

DR-GNN 최신버젼 다운로드

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// repository documentation

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# Distributionally Robust Graph-based Recommendation System (DR-GNN)

![Static Badge](https://img.shields.io/badge/Conference-WWW2024-FF8C00)

This is the PyTorch implementation for our WWW 2024 paper (oral). 
> Bohao Wang, Jiawei Chen, Changdong Li, Sheng Zhou, Qihao Shi, Yang Gao, Yan Feng, Chun Chen, Can Wang 2024. Distributionally Robust Graph-based Recommendation System. [arXiv link](https://arxiv.org/abs/2402.12994)

## Requirements
To install requirements:
```
pip install -r requirements.txt
```

## Training & Evaluation
You can get the results in the paper by running the following code.

**Gowalla**
```
python main.py --model=lgn --enable_DRO=1 --aug_on --full_batch --ood popularity_shift --dataset='gowalla' --weight_decay 0.0001 --alpha 0.04 --tau 1  --aug_coefficient 0.1 --aug_ratio 0.2
```
**Douban**
```
python main.py --model=lgn --enable_DRO=1 --aug_on --full_batch --ood popularity_shift --dataset='douban' --weight_decay 1e-7 --alpha 0.005 --tau 0.1 --aug_coefficient 0.04 --aug_ratio 0.05
```
**Amazon Book**
```
python main.py --model=lgn --enable_DRO=1 --aug_on --full_batch --ood popularity_shift --dataset='amazon-book' --weight_decay 0.0001 --alpha 0.03 --tau 0.8 --aug_coefficient 0.1 --aug_ratio 0.1
```
**Yelp2018**
```
python main.py --model=lgn --enable_DRO=1 --aug_on --full_batch --ood popularity_shift --dataset='yelp2018' --weight_decay 0.0001 --alpha 0.07 --tau 1 --aug_coefficient 0.25 --aug_ratio 0.05
```

## Citation
If you find the paper useful in your research, please consider citing:
```
@inproceedings{wang2024distributionally,
  title={Distributionally Robust Graph-based Recommendation System},
  author={Wang, Bohao and Chen, Jiawei and Li, Changdong and Zhou, Sheng and Shi, Qihao and Gao, Yang and Feng, Yan and Chen, Chun and Wang, Can},
  booktitle={Proceedings of the ACM on Web Conference 2024},
  pages={3777--3788},
  year={2024}
}
```

## Acknowledgments

This project makes use of the following open source projects:

- [LightGCN-PyTorch](https://github.com/gusye1234/LightGCN-PyTorch/tree/master): Some of the functionalities are inspired by this project. Thanks for their great work.

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