DeepCoNN

This is our implementation for the paper:

Lei Zheng, Vahid Noroozi, and Philip S Yu. 2017. Joint deep modeling of users and items using reviews for recommendation. In WSDM. ACM, 425-434.

Two models:

1、DeepCoNN: This is the state-of-the-art method that uti-lizes deep learning technology to jointly model user and itemfrom textual reviews.

2、DeepCoNN++: We extend DeepCoNN by changing its share layer from FM to our neural prediction layer.

The two methods are used as the baselines of our method NARRE in the paper:

Chong Chen, Min Zhang, Yiqun Liu, and Shaoping Ma. 2018. Neural Attentional Rating Regression with Review-level Explanations. In WWW'18.

Please cite our WWW'18 paper if you use our codes. Thanks!

@inproceedings{chen2018neural,
  title={Neural Attentional Rating Regression with Review-level Explanations},
  author={Chen, Chong and Zhang, Min and Liu, Yiqun and Ma, Shaoping},
  booktitle={Proceedings of the 2018 World Wide Web Conference on World Wide Web},
  pages={1583--1592},
  year={2018},
}

Author: Chong Chen ([email protected])

Environments

• python 2.7
• Tensorflow (version: 0.12.1)
• numpy
• pandas

Dataset

In our experiments, we use the datasets from Amazon 5-core(http://jmcauley.ucsd.edu/data/amazon) and Yelp Challenge 2017(https://www.yelp.com/dataset_challenge).

Example to run the codes

Data preprocessing:

python loaddata.py	
python data_pro.py

Train and evaluate the model:

python train.py

Last Update Date: Jan 3, 2018