Introduction

This is the Pytorch implementation for our paper at KDD'23: Knowledge Graph Self-Supervised Rationalization for Recommendation.

Environment Dependencies

You can refer to requirements.txt for the experimental environment we set to use.

run KGRec

Simply use:

python run_kgrec.py --dataset [dataset_name]

And the hyperparameters we use are fixed according to the dataset in KGRec.py.

Baseline Models (KGCL, KGIN)

We also implement KGCL and include the original KGIN release in our repository. For example, to run KGCL, you may execute:

alibaba-ifashion

python run_kgcl.py --mu 0.7 --tau 0.2 --cl_weight 0.1

last-fm

python run_kgcl.py --mu 0.5 --tau 0.1 --cl_weight 0.1

mind

python run_kgcl.py --mu 0.6 --tau 0.2 --cl_weight 0.1

Citation

Please kindly cite our work if you find our paper or codes helpful.

@inproceedings{yang2023knowledge,
  title={Knowledge graph self-supervised rationalization for recommendation},
  author={Yang, Yuhao and Huang, Chao and Xia, Lianghao and Huang, Chunzhen},
  booktitle={Proceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining},
  pages={3046--3056},
  year={2023}
}