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Information 2019, 10(1), 1; https://doi.org/10.3390/info10010001

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.
Received: 9 November 2018 / Revised: 17 December 2018 / Accepted: 18 December 2018 / Published: 20 December 2018
(This article belongs to the Section Artificial Intelligence)
Full-Text   |   PDF [1762 KB, uploaded 20 December 2018]   |  

Abstract

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
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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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.

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