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])
- python 2.7
- Tensorflow (version: 0.12.1)
- numpy
- pandas
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).
Data preprocessing:
python loaddata.py
python data_pro.py
Train and evaluate the model:
python train.py
Last Update Date: Jan 3, 2018