AkiraZC / PPGN

The implementation of "Cross-Domain Recommendation via Preference Propagation GraphNet“, Cheng Zhao, Chenliang Li, Cong Fu, https://doi.org/10.1145/3357384.3358166

PPGN

Codes for CIKM 2019 paper Cross-Domain Recommendation via Preference Propagation GraphNet.

Citation

Please cite our paper if you find this code useful for your research:

@inproceedings{cikm19:ppgn,
  author    = {Cheng Zhao and
               Chenliang Li and
               Cong Fu},
  title     = {Cross-Domain Recommendation via Preference Propagation GraphNet},
  booktitle = {The 28th ACM International Conference on Information and Knowledge Management, {CIKM} 2019, Beijing, China,
               November 3-7, 2019},
  pages     = {2165--2168},
  year      = {2019}
}

Requirement

• Python 3.6
• Tensorflow 1.10.0
• Numpy
• Pandas
• Scipy

Files in the folder

• data/
  • data_prepare.py: constructing cross-domain scenario from overlapping users;
  • dataset.py: defining the class of cross-domain dataset;
• runner/
  • main.py: the main function (including the configurations);
  • model.py: the detail implementation of PPGN;
  • train.py: training and evaluation;
• utils/
  • metrics.py: evaluation metrics.

Running the code

1. Download the original data from Amazon-5core, choose two relevant categories (e.g., Books, Movies and TV) and put them under the same directory in data/.

2. run python data_prepare.py.

3. run python main.py.