KGCL

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}
}

Enviroment Requirement

pip install -r requirements.txt

Dataset

We provide three processed datasets and the corresponding knowledge graphs: Yelp2018 and Amazon-book and MIND.

An example to run KGCL

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

Model Variants

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.