Next Article in Journal
From Homo Sapiens to Robo Sapiens: The Evolution of Intelligence
Previous Article in Journal
Artificial Intelligence and the Limitations of Information
Article

User-Personalized Review Rating Prediction Method Based on Review Text Content and User-Item Rating Matrix

1
School of Computer, Pingdingshan University, Pingdingshan 467000, China
2
Huanghe Science & Technology University, Zhengzhou 450000, China
*
Author to whom correspondence should be addressed.
Information 2019, 10(1), 1; https://doi.org/10.3390/info10010001
Received: 9 November 2018 / Revised: 17 December 2018 / Accepted: 18 December 2018 / Published: 20 December 2018
(This article belongs to the Section Artificial Intelligence)
With the explosive growth of product reviews, review rating prediction has become an important research topic which has a wide range of applications. The existing review rating prediction methods use a unified model to perform rating prediction on reviews published by different users, ignoring the differences of users within these reviews. Constructing a separate personalized model for each user to capture the user’s personalized sentiment expression is an effective attempt to improve the performance of the review rating prediction. The user-personalized sentiment information can be obtained not only by the review text but also by the user-item rating matrix. Therefore, we propose a user-personalized review rating prediction method by integrating the review text and user-item rating matrix information. In our approach, each user has a personalized review rating prediction model, which is decomposed into two components, one part is based on review text and the other is based on user-item rating matrix. Through extensive experiments on Yelp and Douban datasets, we validate that our methods can significantly outperform the state-of-the-art methods. View Full-Text
Keywords: review rating prediction; sentiment classification; user-item matrix; user-personalized model review rating prediction; sentiment classification; user-item matrix; user-personalized model
Show Figures

Figure 1

MDPI and ACS Style

Wang, B.; Chen, B.; Ma, L.; Zhou, G. User-Personalized Review Rating Prediction Method Based on Review Text Content and User-Item Rating Matrix. Information 2019, 10, 1. https://doi.org/10.3390/info10010001

AMA Style

Wang B, Chen B, Ma L, Zhou G. User-Personalized Review Rating Prediction Method Based on Review Text Content and User-Item Rating Matrix. Information. 2019; 10(1):1. https://doi.org/10.3390/info10010001

Chicago/Turabian Style

Wang, Bingkun, Bing Chen, Li Ma, and Gaiyun Zhou. 2019. "User-Personalized Review Rating Prediction Method Based on Review Text Content and User-Item Rating Matrix" Information 10, no. 1: 1. https://doi.org/10.3390/info10010001

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop