Recommender System using HS-GCN model

This is the project submission codes for semester project for GML-FA course IIT Kharagpur

By Ramashish Gupta, Shakti Prasad Nanda and Shashank Sundi

colab link

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

File specification

• preprocessing.py : loads the raw data in path ./raw_data, and the results are saved in path ./para and then 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: preprocessing.py--->model_train.py--->model_test.py

Credits

• Original Implementation link