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

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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.

About

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

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