RetaGNN: Relational Temporal Attentive Graph Neural Networks for Holistic Sequential Recommendation

Pytorch based implemention of Relational Temporal Attentive Graph NeuralNetworks for recommender systems, based on our paper:

Cheng HSU, Cheng-Te Li, Relational Temporal Attentive Graph NeuralNetworks (2021)

Requirements

• Python 3.6
• Pytorch (1.4)

Usage

To reproduce the experiments mentioned in the paper you can run the following command:

python train.py

Note: .

Cite

Please cite our paper if you use this code in your own work:

@article{vdberg2021graph,
  title={RetaGNN: Relational Temporal Attentive Graph Neural Networks for Holistic Sequential Recommendationn},
  author={Cheng Hsu and Cheng-Te Li},
  journal={arXiv preprint arXiv:2101.12457},
  year={2021}
}