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[TKDE 2022] The source code of "Dynamic Graph Neural Networks for Sequential Recommendation"

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CRIPAC-DIG/DGSR

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DGSR

model

This is the code for the TKDE 2022 Paper: Dynamic Graph Neural Networks for Sequential Recommendation.

Usage

Generate data

You need to run the file new_data.py to generate the data format needed for our model. The detailed commands can be found in load_{dataset}.sh

You need to run the file generate_neg.py to generate data to speed up the test. You can set the data set in the file.

Training and Testing

Then you can run the file new_main.py to train and test our model. The detailed commands can be found in {dataset}.sh

Requirements

  • Python 3.6
  • torch 1.7.1
  • dgl 0.7.2

Citation

Please cite our paper if you use the code:

@ARTICLE{9714053,
  author={Zhang, Mengqi and Wu, Shu and Yu, Xueli and Liu, Qiang and Wang, Liang},
  journal={IEEE Transactions on Knowledge and Data Engineering}, 
  title={Dynamic Graph Neural Networks for Sequential Recommendation}, 
  year={2022},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TKDE.2022.3151618}}