CG-KGR

This is the Tensorflow implementation for paper "Attentive Knowledge-aware Graph Convolutional Networks with Collaborative Guidance for Personalized Recommendation." arxiv. Yankai Chen, Yaming Yang, Yujing Wang, Jing Bai, Xiangchen Song, and Irwin King. 2022.

Environment Requirement

The code runs well under python 3.8.0. The required packages are as follows:

• Tensorflow-gpu == 1.14.0
• numpy == 1.21.1

Datasets

First, please refer to link to download the orginal rating data and distribute them under "/CG-KGR/data/". Then create datasplit under the ratio 6:2:2.

For example, uncomment the following line in main_movie.py:

data_split(args)

then uncomment the following line:

Exp_run(args)

and Finally run [main_xxx.py] as:

python main_movie.py

You can also download our five experimental random splits for each datasets via link.

Citation

If you find this paper useful for your research, please kind cite it as:

@inproceedings{chen2021attentive,
  title={Attentive Knowledge-aware Graph Convolutional Networks with Collaborative Guidance for Personalized Recommendation},
  author={Chen, Yankai and Yang, Yaming and Wang, Yujing and Bai, Jing and Song, Xiangchen and King, Irwin},
  booktitle={The 38th IEEE International Conference on Data Engineering},
  pages={299--311},
  publisher={{IEEE}},
  year={2022},

}