This is the Pytorch implementation for our SIGIR'22 paper: Knowledge Graph Contrastive Learning for Recommendation. The CF learning part in the code is based on the open-source repository here: LightGCN, many thanks to the authors!
You are welcome to cite our paper:
@inproceedings{kgcl2022,
author = {Yang, Yuhao and Huang, Chao and Xia, Lianghao and Li, Chenliang},
title = {Knowledge Graph Contrastive Learning for Recommendation},
year = {2022},
booktitle = {Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {1434–1443}
}
pip install -r requirements.txt
We provide three processed datasets and the corresponding knowledge graphs: Yelp2018 and Amazon-book and MIND.
run KGCL on Yelp2018 dataset:
- change base directory
Change ROOT_PATH in code/world.py
- command
cd code && python main.py --dataset=yelp2018
cd code && python main.py --dataset=amazon-book
cd code && python main.py --dataset=MIND
We also simply implement LightGCN (SIGIR'20) and SGL (SIGIR'21) for easy comparison. You can test these models implemented here by:
cd code && python main.py --dataset=yelp2018 --model=lgn
and
cd code && python main.py --dataset=yelp2018 --model=sgl
However, we still recommend to also refer to the authors' official implementation to avoid potential performance problems.