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).

This codebase was adapted from LightGCN-pytorch.

Enviroment 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}
}