A-PGNN

The code and dataset for our paper: Personalizing Graph Neural Networks with Attention Mechanism for Session-based Recommendation (https://arxiv.org/abs/1910.08887), which has been submitted to TKDE. We have implemented our methods in Tensorflow.

Here are two datasets we used in our paper.

The processed data can be downloaded: https://www.dropbox.com/sh/hwx2347ir1worag/AABJK6IBXHNBlbvrvKqw94YKa?dl=0

Usage

Generate data

You need to run the file record.py first to preprocess the data to generate the tf.record formart data for training and test.

For example: python record.py --dataset=all_data --data=xing --adj=adj_all --max_session=50. This will creat xing/ in datasets/.

This code can be given the following command-line arguments:

--dataset: choose to use fully data or samples, if all_data: use fully data, if None or sample: use sample data.

--data: the name of data set, we can choose xing or reddit.

--graph: graph neural network, default set is ggnn.

--max_session: the maximum length of historical sessions.

--max_length: the maximum length of current session.

--last: if True, the last one for testing, else, the next 20% for testing.

Training and Testing

Then you can run the file train_last.py to train the model and test.

For example: python train_last.py --data=xing --mode=transformer --user_ --adj=adj_all --dataset=all_data --hiddenSize=100

This code can be given the following command-line arguments:

--dataset: choose to use fully data or samples, if all_data: use fully data, if None or sample: use sample data.

--data: the name of data set, we can choose xing or reddit.

--user_: whether to use user embedding.

--max_session: the maximum length of historical sessions.

--max_length: the maximum length of current session.

--adj: if adj_all, use normalized weights for adjacency matrices, else, use binary adjacency matrix.

--batchSize: batchsize.

--epoch: epoch.

--lr: learning rate.

--buffer_size: the maximum number of elements that will be added to the buffer. For details, see the use of tf.record, for Xing, we set 200000, Reddit is 100000.

Requirement

Python 3.6.5
Tensorflow-gpu 1.10.0

Cite

  @article{wu2019personalizing,
  title={Personalizing Graph Neural Networks with Attention Mechanism for Session-based Recommendation},
  author={Wu, Shu and Zhang, Mengqi and Jiang, Xin and Ke, Xu and Wang, Liang},
  journal={arXiv preprint arXiv:1910.08887},
  year={2019}
}