/HS-GCN

Primary LanguagePythonGNU General Public License v3.0GPL-3.0

HS-GCN

This is our experiment codes for the paper:

HS-GCN: Hamming Spatial Graph Convolutional Networks for Recommendation.

Environment settings

  • Python 3.7
  • Pytorch 1.4.0
  • PyTorch Geometric 1.6.1
  • Numpy 1.19.5
  • Pandas 1.1.3

File specification

  • data_load.py : loads the raw data in path ./raw_data, and the results are saved in path ./para.
  • data_triple.py : obtains the triplets for model training, and the results are saved in path ./para.
  • HSGCN_model.py : implements the model framework of HS-GCN.
  • model_train.py : the training process of model.
  • model_test.py : the testing process of model.

Usage

  • Execution sequence

    The execution sequence of codes is as follows: data_load.py--->data_triple.py--->model_train.py--->model_test.py

  • Execution results

    During the execution of file model_train.py, the epoch, iteration, and training loss will be printed as the training process:

    [1,   600] loss: 1.21214
    [1,  1200] loss: 1.19586
    [1,  1800] loss: 1.18090
    [2,   600] loss: 1.13528
    [2,  1200] loss: 1.12297
    [2,  1800] loss: 1.11104
    [3,   600] loss: 1.07233
    [3,  1200] loss: 1.06290
    [3,  1800] loss: 1.05153
    ...
    

    File model_test.py should be executed after the training process, and the performance of HS-GCN will be printed:

    HR@50: 0.2052; NDCG@50: 0.3081; P@50: 0.2020