Intention Adaptive Graph Neural Network (IAGNN)

This is the official repository of paper Intention Adaptive Graph Neural Network for Category-Aware Session-Based Recommendation.

If you found this work helpful, please kindly cite the paper as follows:

@inproceedings{DBLP:conf/dasfaa/CuiSZPZGW22,
  author    = {Chuan Cui and
               Qi Shen and
               Shixuan Zhu and
               Yitong Pang and
               Yiming Zhang and
               Hanning Gao and
               Zhihua Wei},
  editor    = {Arnab Bhattacharya and
               Janice Lee and
               Mong Li and
               Divyakant Agrawal and
               P. Krishna Reddy and
               Mukesh K. Mohania and
               Anirban Mondal and
               Vikram Goyal and
               Rage Uday Kiran},
  title     = {Intention Adaptive Graph Neural Network for Category-Aware Session-Based
               Recommendation},
  booktitle = {Database Systems for Advanced Applications - 27th International Conference,
               {DASFAA} 2022, Virtual Event, April 11-14, 2022, Proceedings, Part
               {II}},
  series    = {Lecture Notes in Computer Science},
  volume    = {13246},
  pages     = {150--165},
  publisher = {Springer},
  year      = {2022},
  url       = {https://doi.org/10.1007/978-3-031-00126-0\_10},
  doi       = {10.1007/978-3-031-00126-0\_10},
  timestamp = {Fri, 29 Apr 2022 14:50:39 +0200},
  biburl    = {https://dblp.org/rec/conf/dasfaa/CuiSZPZGW22.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Prerequisite

Install the dependencies by conda

dgl~=0.6.0.post1
ipdb~=0.13.9
numpy~=1.21.2
pretty_errors~=1.2.24
PyMySQL~=1.0.2
scikit_learn~=1.0.2
torch~=1.8.1
TorchSnooper~=0.8
tqdm~=4.62.3

or by pip:

pip install -r requirements.txt

Dataset

GoogleDrive or BaiduPan (提取码：2jd1)

Put the downloaded *.pkl files by following this file structure:

|--dataset
   |--diginetica_x
      |--train.pkl
      |--test.pkl
   |--jdata_cd
      |--train.pkl
      |--test.pkl
   |--yc_BT_4
      |--train.pkl
      |--test.pkl
|--IAGNN	# Souce code of this repository
   |--train.py
   |--IAGNN.py
   ...

How to train

# JData
python train.py --lr=0.003 --lr_step=2 --GL=3 --dataset=jdata_cd
# Yoochoose
python train.py --lr=0.001 --lr_step=1 --GL=1 --dataset=yc_BT_4
# Diginetica
python train.py --lr=0.003 --lr_step=1 --GL=2 --dataset=diginetica_x