A reimplementation of SRGNN.

(WARNING: The computation of session embedding only uses embedding W.R.T. nodes in a session graph while in the paper, the calculation based on the whole session sequence, which means they may calculate re-occur items as many times as they occue.)

Original code from here. Original paper.

Borrow the data preprocessing from original repo, including diginetica and yoochoose.

Using PyTorch 1.0, TensorboardX and PyTorch-Geometric.

1. Follow the steps in original code repo to get train.txt and test.txt for every dataset.

2. Put both txt file in the raw folder W.R.T. different datasets.

cd src
python main.py --dataset=diginetica

If you use make advantage of the SR-GNN model in your research, please cite the following:

@inproceedings{Wu:2019vb,
author = {Wu, Shu and Tang, Yuyuan and Zhu, Yanqiao and Wang, Liang and Xie, Xing and Tan, Tieniu},
title = {Session-based Recommendation with Graph Neural Networks},
booktitle = {Proceedings of The Twenty-Third AAAI Conference on Artificial Intelligence},
series = {AAAI '19},
year = {2019},
url = {http:https://arxiv.org/abs/1811.00855}
}