RNS

This repository is the implementation of RNS (arXiv):

A Review-Driven Neural Model for Sequential Recommendation (IJCAI 2019) Chenliang Li, Xichuan Niu, Xiangyang Luo, Zhenzhong Chen, Cong Quan

Requirements

• Python 3.6
• PyTorch 0.4
• Numpy
• Pandas
• SciPy

Files in the folder

• data/
  • reviews_Amazon_Instant_Video.json/
    • video_train.csv: csv file (user_id, item_id, rating, timestamp) for training
    • video_test.csv: csv file (user_id, item_id, rating, timestamp) for testing
    • vocabulary: vocabulary of user reviews text
    • u_text: review documents written by user u
    • i_text: review documents written for item i

Running the code

1. Install required packages.
2. run python train_model.py

Citation

Please cite our paper if you find this code useful for your research:

@inproceedings{RNS2019,
  author    = {Chenliang Li and
               Xichuan Niu and
               Xiangyang Luo and
               Zhenzhong Chen and
               Cong Quan},
  title     = {A Review-Driven Neural Model for Sequential Recommendation},
  booktitle = {Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, {IJCAI} 2019},
  pages     = {2866--2872},
  year      = {2019}
}

Acknowledgment

This source code is built on top of caser_pytorch. We thank the author for his amazing work.