A simple, efficient and effective Jacobi polynomial-based graph collaborative filtering algorithm built on recbole.
conda env create -f environment.yaml
python run.py --dataset gowalla
For large scale datasets, you need to downlowd tha dataset to use.
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, you can download alibaba.zip from Google Drive. Then
mv alibaba.zip dataset
unzip alibaba.zip
python run.py --dataset alibaba
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 |
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},
}