A-PGNN

This is the code for the TKDE Paper: Personalized Graph Neural Networks with Attention Mechanism for Session-Aware Recommendation.

Usage

Preparation data and code

Here are two datasets we used in our paper.

• Xing http://2016.recsyschallenge.com/
• Reddit https://www.kaggle.com/colemaclean/subreddit-interactions

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

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.

Requirements

• Python 3.6.5
• Tensorflow-gpu 1.10.0

Citation

Please cite our paper if you use the code:

@article{zhang2020personalized,
  title={Personalized Graph Neural Networks with Attention Mechanism for Session-Aware Recommendation},
  author={Zhang, Mengqi and Wu, Shu and Gao, Meng and Jiang, Xin and Xu, Ke and Wang, Liang},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2020},
  publisher={IEEE}
}