Channel-Independent Graph Convolutional Network (CIGCN)

This is our Tensorflow implementation for the paper:

Tianyu Zhu, Leilei Sun, and Guoqing Chen. "Embedding Disentanglement in Graph Convolutional Networks for Recommendation." IEEE Transactions on Knowledge and Data Engineering (TKDE) (2021).

Introduction

Channel-Independent Graph Convolutional Network (CIGCN) is a graph convolution-based recommendation framework that adopts diagonal filter matrices for learning disentangled user and item embeddings.

Citation

@article{zhu2021embedding,
  title={Embedding Disentanglement in Graph Convolutional Networks for Recommendation},
  author={Zhu, Tianyu and Sun, Leilei and Chen, Guoqing},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2021},
  publisher={IEEE}
}

Environment Requirement

The code has been tested running under Python 3.6. The required packages are as follows:

• tensorflow == 1.5.0
• numpy == 1.14.2
• scipy == 1.1.0

Dataset

• Download: Google Drive

Example to Run the Codes

• Amazon Automotive dataset

python main.py --dataset=Automotive