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ICDE-2020-SMGCN

This is our Tensorflow implementation for the ICDE-2020 paper:

Syndrome-aware Herb Recommendation with Multi-Graph Convolution Network

We reference the public code from https://github.com/xiangwang1223/neural_graph_collaborative_filtering

Citation

If you want to use our codes and datasets in your research, please cite:

Jin Y, Zhang W, He X, et al. Syndrome-aware herb recommendation with multi-graph convolution network[C]//2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, 2020: 145-156.

Environment Requirement

The code has been tested running under Python 3.6.5. The required packages are as follows:

  • tensorflow == 1.8.0
  • numpy == 1.14.3
  • scipy == 1.1.0
  • sklearn == 0.19.1

Dataset

  • train.txt

    • Train file.
    • Each line is a prescription split by '\t', with the former part is symptoms split by space and the later part is herbs split by space.

  • test.txt

    • Test file.
    • Each line is a prescription split by '\t', with the former part is symptoms split by space and the later part is herbs split by space.

  • symPair-5.txt

    • sym-pair file
    • Each line is a sym pair.

  • herbPair-40.txt

    • herb-pair file
    • Each line is a herb pair.

Example to Run the Codes

see the SMGCN.sh file