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JGCF

A simple, efficient and effective Jacobi polynomial-based graph collaborative filtering algorithm built on recbole.

Requirements

conda env create -f environment.yaml

Quich Start

python run.py --dataset gowalla

Datasets

For large scale datasets, you need to downlowd tha dataset to use.

For Amazon_Books

For alibaba, you can download Amazon_Books.zip from Google Drive. Then

mkdir dataset/Amazon_Books
mv Amazon_Books.zip dataset/Amazon_Books
unzip Amazon_Books.zip
python run.py --dataset Amazon_Books

For Alibaba-iFashion

For alibaba, you can download alibaba.zip from Google Drive. Then

mv alibaba.zip dataset
unzip alibaba.zip
python run.py --dataset alibaba

Benchmarking

Gowalla:

Metrics LightGCN (K=3) JGCF (K=3)
Recall@10 0.1382 0.1574
NDCG@10 0.1003 0.1145
Recall@20 0.1983 0.2232
NDCG@20 0.1175 0.1332
Recall@50 0.3067 0.3406
NDCG@50 0.1438 0.1619

Citation

If you find our work useful, please cite:

@inproceedings{
    jgcf2023on,
    title={On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based Graph Collaborative Filtering},
    author={Jiayan Guo, Lun Du, Xu Chen, Xiaojun Ma, Qiang Fu, Shi Han, Dongmei Zhang, Yan Zhang},
    booktitle={29th SIGKDD Conference on Knowledge Discovery and Data Mining},
    year={2023},
}