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

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.

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

• 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.

see the SMGCN.sh file