https://github.com/hkuds/gte
[CIKM'2023] "GTE: How Expressive are Graph Neural Networks for Recommendation?"
https://github.com/hkuds/gte
collaborative-filtering graph-neural-networks recommender-systems
Last synced: 2 months ago
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[CIKM'2023] "GTE: How Expressive are Graph Neural Networks for Recommendation?"
- Host: GitHub
- URL: https://github.com/hkuds/gte
- Owner: HKUDS
- Created: 2023-08-07T06:36:03.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2023-08-23T12:45:58.000Z (about 2 years ago)
- Last Synced: 2025-07-04T00:09:18.673Z (4 months ago)
- Topics: collaborative-filtering, graph-neural-networks, recommender-systems
- Language: Python
- Homepage: https://arxiv.org/pdf/2308.11127.pdf
- Size: 16.4 MB
- Stars: 19
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# GTE
This is the Python implementation for the GTE algorithm in the paper **How Expressive are Graph Neural Networks in Recommendation?**, *ACM International Conference on Information and Knowledge Management*, 2023. [[arXiv]](https://arxiv.org/abs/2308.11127)
#### How to run the codes
```
python main.py --data [amazon_beauty/sparse_tmall/douban/gowalla/tmall/yelp]
```
#### Citing our paper
Please kindly cite our paper if you find this paper and the codes helpful.
```
@inproceedings{cai2023expressive,
title={How Expressive are Graph Neural Networks in Recommendation?},
author={Cai, Xuheng and Xia, Lianghao and Ren, Xubin and Huang, Chao},
booktitle={Proceedings of the 32nd ACM International Conference on Information and Knowledge Management},
year={2023}
}
```