TheWebConf24-LTGNN-PyTorch

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TheWebConf24-LTGNN-PyTorch

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TheWebConf'24 full paper - "Linear-Time Graph Neural Networks for Scalable Recommendations"

TheWebConf24-LTGNN-PyTorch 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

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## LTGNN-PyTorch

This is the Pytorch implementation for our TheWebConf'24 paper "**Linear-Time Graph Neural Networks for Scalable Recommendations**". Please find our paper in
ACM Digital Library (https://dl.acm.org/doi/10.1145/3589334.3645486) or arXiv (https://arxiv.org/abs/2402.13973).

The [Amazon-Large](https://drive.google.com/file/d/13pYTb6FqdfaDy3WC-Ax-ruK1q9_d0Qub/view?usp=drive_link) dataset is available on Google Drive. If you prefer to build it yourself, please refer to Section 4.1 of our paper for instructions on constructing it directly from the [Amazon Review Data](https://cseweb.ucsd.edu/~jmcauley/datasets/amazon_v2/).

This codebase was adapted from [LightGCN-pytorch](https://github.com/gusye1234/LightGCN-PyTorch).

For additional technical details and theoretical analysis, including the reduced neighbor sampling variance and the convergence of the forward process to PPNP, please refer to my [master's thesis](https://theses.lib.polyu.edu.hk/handle/200/13533).

## Environment Requirement

`pip install -r requirements.txt`

## Command

` cd code && python main.py --decay=2e-4 --lr=0.0015 --layer=1 --seed=2020 --dataset="yelp2018" --topks="[20]" --recdim=64 --model="ltgnn" --appnp_alpha=0.45 --num_neighbors=15 --device=0`

` cd code && python main.py --decay=2e-4 --lr=0.0015 --layer=1 --seed=2020 --dataset="alibaba-ifashion" --topks="[20]" --recdim=64 --model="ltgnn" --appnp_alpha=0.45 --num_neighbors=15 --device=0`

## BibTeX
If you find **LTGNN** useful in your research, please cite the following in your manuscript:

```
@inproceedings{zhang2024linear,
  title={Linear-Time Graph Neural Networks for Scalable Recommendations},
  author={Zhang, Jiahao and Xue, Rui and Fan, Wenqi and Xu, Xin and Li, Qing and Pei, Jian and Liu, Xiaorui},
  booktitle={Proceedings of the ACM on Web Conference 2024},
  pages = {3533-3544},
  year={2024}
}

@article{zhang2024towards,
  title={Towards efficient graph neural networks for large-scale recommender systems},
  author={Zhang, Jiahao},
  year={2024},
  publisher={Hong Kong Polytechnic University}
}
```

TheWebConf24-LTGNN-PyTorch

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