基于图神经网络的会话推荐内含数据集和教程.zip

# SR-GNN ## Paper data and code This is the code for the AAAI 2019 Paper: [Session-based Recommendation with Graph Neural Networks](https://arxiv.org/abs/1811.00855). We have implemented our methods in both **Tensorflow** and **Pytorch**. 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> There is a small dataset `sample` included in the folder `datasets/`, which can be used to test the correctness of the code. We have also written a [blog](https://sxkdz.github.io/research/SR-GNN) explaining the paper. ## Usage You need to run the file `datasets/preprocess.py` first to preprocess the data. For example: `cd datasets; python preprocess.py --dataset=sample` ```bash usage: preprocess.py [-h] [--dataset DATASET] optional arguments: -h, --help show this help message and exit --dataset DATASET dataset name: diginetica/yoochoose/sample ``` Then you can run the file `pytorch_code/main.py` or `tensorflow_code/main.py` to train the model. For example: `cd pytorch_code; python main.py --dataset=sample` You can add the suffix `--nonhybrid` to use the global preference of a session graph to recommend instead of the hybrid preference. You can also change other parameters according to the usage: ```bash usage: main.py [-h] [--dataset DATASET] [--batchSize BATCHSIZE] [--hiddenSize HIDDENSIZE] [--epoch EPOCH] [--lr LR] [--lr_dc LR_DC] [--lr_dc_step LR_DC_STEP] [--l2 L2] [--step STEP] [--patience PATIENCE] [--nonhybrid] [--validation] [--valid_portion VALID_PORTION] optional arguments: -h, --help show this help message and exit --dataset DATASET dataset name: diginetica/yoochoose1_4/yoochoose1_64/sample --batchSize BATCHSIZE input batch size --hiddenSize HIDDENSIZE hidden state size --epoch EPOCH the number of epochs to train for --lr LR learning rate --lr_dc LR_DC learning rate decay rate --lr_dc_step LR_DC_STEP the number of epochs after which the learning rate decay --l2 L2 l2 penalty --step STEP gnn propogation steps --patience PATIENCE the number of epoch to wait before early stop --nonhybrid only use the global preference to predict --validation validation --valid_portion VALID_PORTION split the portion of training set as validation set ``` ## Requirements - Python 3 - PyTorch 0.4.0 or Tensorflow 1.9.0 ## Other Implementation for Reference There are other implementation available for reference: - Implementation based on PaddlePaddle by Baidu [[Link]](https://github.com/PaddlePaddle/models/tree/develop/PaddleRec/gnn) - Implementation based on PyTorch Geometric [[Link]](https://github.com/RuihongQiu/SR-GNN_PyTorch-Geometric) - Another implementation based on Tensorflow [[Link]](https://github.com/jimanvlad/SR-GNN) - Yet another implementation based on Tensorflow [[Link]](https://github.com/loserChen/TensorFlow-In-Practice/tree/master/SRGNN) ## Citation Please cite our paper if you use the code: ``` @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}, } ```