# dgrec-python

**Repository Path**: vvvvweivv/dgrec-python

## Basic Information

- **Project Name**: dgrec-python
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-03-14
- **Last Updated**: 2024-03-25

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# DGRec

A PyTorch and DGL implementation for the WSDM 2023 paper below:  
[DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation](https://arxiv.org/pdf/2211.10486.pdf)

Environment
DGL version 1.0.1
Pytorch version 1.12.1

## Running
``python main.py``  
Then you can get similar result on TaoBao dataset as illustrated in the paper.  

You can check different hyper-parameters in `utils/parser.py`

## Dataset
Format of train.txt val.txt test.txt: UserID,ItemID.

Format of item_category.txt: ItemID,CategoryID

If you want to change to your own dataset. Format your dataset in a folder as in datasets/Beauty, and specify your folder name in --dataset argument.

## Citation
If you use our code, please cite the paper below:
```bibtex
@inproceedings{yang2023dgrec,
  title={DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation},
  author={Yang, Liangwei and Wang, Shengjie and Tao, Yunzhe and Sun, Jiankai and Liu, Xiaolong and Yu, Philip S and Wang, Taiqing},
  booktitle={Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining},
  pages={661--669},
  year={2023}
}
```