RGNN

This is the implementation of the following paper:

Yong Liu, Susen Yang, Yinan Zhang, Chunyan Miao, Zaiqing Nie, and Juyong Zhang. "Learning Hierarchical Review Graph Representations for Recommendation." IEEE Transactions on Knowledge and Data Engineering (2021).

Environment Requirement

The code has been tested running under Python 3.5. The required packages are as follows:

• pytorch
• pytorch-geometric
• nltk
• numpy
• scipy
• networkx

Example to Run the Codes

The instruction of commands can be found in the source codes (see main function in model/train.py).

• python train.py
• python train.py --dataset music --batch_size 128 --num_layers 2 --dim 16 --word_dim 16 --hidd_dim 8 --factors 8 --lr 0.005 --l2_re 0.01 --epochs 100 --dropout 0

Dataset (data/music)

• data.train, data.eval, data.test

  • Training, Validation, Testing rating file.
  • Each line is a triple: ('User ID' 'Item ID' 'Rating').

• data.para

  • statistics of data.

• data.user_graph, data.item_graph

  • Review graph file of the user, item.

Citation

@article{liu2021learning,
  title={Learning Hierarchical Review Graph Representations for Recommendation},
  author={Liu, Yong and Yang, Susen and Zhang, Yinan and Miao, Chunyan and Nie, Zaiqing and Zhang, Juyong},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2021},
  publisher={IEEE}
}