tseesurenb / dgsr

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

DGSR

The code and dataset for our TKDE 2022 paper: Dynamic Graph Neural Networks for Sequential Recommendation (https://ieeexplore.ieee.org/abstract/document/9714053). We have implemented our methods in Pytorch.

Dependencies

• Python 3.6
• torch 1.7.1
• dgl 0.7.2

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

Cite

If you want to use our codes in your research, please cite:

@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}}