This code is for the paper "Graph Neural Networks with Dynamic and Static Representations for Social Recommendation" which is accepted by DASFAA 2022.

Lin, J., Chen, S., Wang, J. (2022). Graph Neural Networks with Dynamic and Static Representations for Social Recommendation. In: , et al. Database Systems for Advanced Applications. DASFAA 2022. Lecture Notes in Computer Science, vol 13246. Springer, Cham. https://doi.org/10.1007/978-3-031-00126-0_18

This paper proposes a PyTorch framework called GNN-DSR for social recommendation.

If you use our works and codes in your research, please cite:

@inproceedings{lin2022gnndsr,
    title="Graph Neural Networks with Dynamic and Static Representations for Social Recommendation",
    author={Lin, Junfa and Chen, Siyuan and Wang, Jiahai},
    booktitle={Database Systems for Advanced Applications},
    year={2022},
    publisher={Springer International Publishing},
    address={Cham},
    pages={264--271},
    isbn={978-3-031-00126-0}
}

• Python 3.8
• CUDA 11.3
• PyTorch 1.8.1
• NumPy 1.19.2
• Pandas 1.1.3
• tqdm 4.50.2

1. Install all the requirements.

2. Train and evaluate the GNN-DSR using the Python script main.py.
   To reproduce the results on Ciao in our paper, you can run

   python main.py --test

   To see the detailed usage of main.py, you can run

   python main.py -h

3. Preprocess the datasets using the Python script preprocess.py.
   For example, to preprocess the Ciao dataset, you can run

   python preprocess.py --dataset Ciao

   The above command will store the preprocessed data files in folder datasets/Ciao.

   Raw Datasets (Ciao and Epinions) can be downloaded at http://www.cse.msu.edu/~tangjili/trust.html

   To see the detailed usage of preprocess.py, you can run

   python preprocess.py -h

If you cannot download the documents of preprocessed data and weights, you can try to download them at Google Drive