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MVGAE: Multi-modal Variational Graph Auto-encoder for Recommendation Systems

This is our implementation of MVGAE for recommendation systems associated with:

MVGAE: Multi-modal Variational Graph Auto-encoder for Recommendation Systems,
Jing Yi and Zhenzhong Chen

Environment Requirement

• Pytorch == 1.4.0
• torch-cluster == 1.5.4
• torch-geometric == 1.4.1
• torch-scatter == 2.0.4
• torch-sparse == 0.6.1
• torch-spline-conv == 1.2.1

Model

• BaseModel.py: Implementation of graph convolutional operator using Pytorch Geometric library.
• Model.py: Implementation of graph convolutional networks (GCNs), Product-of-exprets (PoE) and our MVGAE.

If you find our codes helpful, please kindly cite the following paper. Thanks!

@article{mvgae,
  title={Multi-modal Variational Graph Auto-encoder for Recommendation Systems},
  author={Yi, Jing and Chen, Zhenzhong},
  year={2021},
}