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.
- Python 3.6
- torch 1.7.1
- dgl 0.7.2
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.
Then you can run the file new_main.py
to train and test our model.
The detailed commands can be found in {dataset}.sh
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}}