xuguangning1218/intgnn_icassp2023

Official source code for paper 《Int-GNN: a User Intention Aware Graph Neural Network for Session-Based Recommendation》accepted by ICASSP 2023

IntGNN

Official source code for paper Int-GNN: a User Intention Aware Graph Neural Network for Session-Based Recommendation accepted by ICASSP 2023

Overall Architecture of IntGNN

Environment Setting

pytorch == 1.12.0
numpy == 1.20.3
tqdm == 4.61.2
torchvision == 0.13.0

Source Files Description

-- 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

Run

When the environment and datasets are cloned, you can train the IntGNN by running the following code:

cd ./pytorch_code
python train_intgnn.py

Citation

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}}