Official source code for paper Int-GNN: a User Intention Aware Graph Neural Network for Session-Based Recommendation accepted by ICASSP 2023
pytorch == 1.12.0
numpy == 1.20.3
tqdm == 4.61.2
torchvision == 0.13.0
-- datasets # dataset folder
-- diginetica # diginetica dataset
-- retailRocket_DSAN # retail dataset
-- Tmall # Tmall dataset
-- figure # figure provider
-- model.jpg # architecture of Int-GNN model
-- pytorch_code # main code of the project
-- utils # the utils file folder
-- models # the models file folder
-- controller.py # the basic control operation
-- train_intgnn.py # the core code of the Int-GNN
When the environment and datasets are cloned, you can train the IntGNN by running the following code:
cd ./pytorch_code
python train_intgnn.py
If you find this code or idea useful, please cite our work:
@INPROCEEDINGS{xu2023Int,
author={Xu, Guangning and Yang, Jinyang and Guo, Jinjin and Huang, Zhichao and Zhang, Bowen},
booktitle={ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
title={Int-GNN: A User Intention Aware Graph Neural Network for Session-Based Recommendation},
year={2023},
volume={},
number={},
pages={1-5},
doi={10.1109/ICASSP49357.2023.10097031}}