This is an implementation of this paper(SR-GNN) based on Tensorflow 2.X, which contains the extra functions as below:
TensorFlow 2.X (version>=2.10 is prefer)
Python 3.9
CUDA11.6 and above is prefer
cudnn8.7.0 and above is prefer
Caution: For who wants to run in native-Windows, TensorFlow 2.10 was the last TensorFlow release that supported GPU on native-Windows.
This is the code for the AAAI 2019 Paper: Session-based Recommendation with Graph Neural Networks.
Here are two datasets we used in our paper. After downloaded the datasets, you can put them in the folder datasets/:
- YOOCHOOSE: http://2015.recsyschallenge.com/challenge.html or https://www.kaggle.com/chadgostopp/recsys-challenge-2015
- DIGINETICA: http://cikm2016.cs.iupui.edu/cikm-cup or https://competitions.codalab.org/competitions/11161
Here is a blog explaining the paper.
@inproceedings{Wu:2019ke,
title = {{Session-based Recommendation with Graph Neural Networks}},
author = {Wu, Shu and Tang, Yuyuan and Zhu, Yanqiao and Wang, Liang and Xie, Xing and Tan, Tieniu},
year = 2019,
booktitle = {Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence},
location = {Honolulu, HI, USA},
month = jul,
volume = 33,
number = 1,
series = {AAAI '19},
pages = {346--353},
url = {https://aaai.org/ojs/index.php/AAAI/article/view/3804},
doi = {10.1609/aaai.v33i01.3301346},
editor = {Pascal Van Hentenryck and Zhi-Hua Zhou},
}