/SRGNN_PyG

A reimplementation of SRGNN.

Primary LanguagePython

SRGNN_PyG

A reimplementation of SRGNN.

image

Original code from here. Original paper.

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

Using PyTorch 1.2.0, PyTorch-Geometric 1.3.1 and tqdm.

Data preparation

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

  2. Put both tain.txt and test.txt in the raw folder W.R.T. different datasets.

Training and testing

cd src
python main.py --dataset=diginetica

Citation

If you 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://arxiv.org/abs/1811.00855}
}