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

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Overview

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}
}
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Comments
  • no data files

    no data files

    FileNotFoundError: [Errno 2] No such file or directory: '/home/hsucheng/DRS/dataset/RS/ny.checkin'

    Is there any pre-process code and data to make the code running? thx a lot

    opened by lzh66 3
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