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A Recommendation Approach Based on Product Attribute Reviews: Improved Collaborative Filtering Considering the Sentiment Polarity

Min Cao1, Sijing Zhou1, Honghao Gao1,2,3

1 School of Computer Engineering and Science, Shanghai University, Shanghai, China;
2 Computing Center, Shanghai University, Shanghai, China;
3 Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai, China;

* Corresponding Author: Honghao Gao, email

Intelligent Automation & Soft Computing 2019, 25(3), 595-604. https://doi.org/10.31209/2019.100000114

Abstract

Recommender methods using reviews have become an area of active research in e-commerce systems. The use of auxiliary information in reviews as a way to effectively accommodate sparse data has been adopted in many fields, such as the product field. The existing recommendation methods using reviews typically employ aspect preference; however, the characteristics of product reviews are not considered adequate. To this end, this paper proposes a novel recommendation approach based on using product attributes to improve the efficiency of recommendation, and a hybrid collaborative filtering is presented. The product attribute model and a new recommendation ranking formula are introduced to implement recommendation using reviews. Experimental results show that the proposed method outperforms baselines in terms of sparse data.

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Cite This Article

APA Style
Cao, M., Zhou, S., Gao, H. (2019). A recommendation approach based on product attribute reviews: improved collaborative filtering considering the sentiment polarity. Intelligent Automation & Soft Computing, 25(3), 595-604. https://doi.org/10.31209/2019.100000114
Vancouver Style
Cao M, Zhou S, Gao H. A recommendation approach based on product attribute reviews: improved collaborative filtering considering the sentiment polarity. Intell Automat Soft Comput . 2019;25(3):595-604 https://doi.org/10.31209/2019.100000114
IEEE Style
M. Cao, S. Zhou, and H. Gao "A Recommendation Approach Based on Product Attribute Reviews: Improved Collaborative Filtering Considering the Sentiment Polarity," Intell. Automat. Soft Comput. , vol. 25, no. 3, pp. 595-604. 2019. https://doi.org/10.31209/2019.100000114



cc Copyright © 2019 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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