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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,

Intelligent Automation & Soft Computing 2019, 25(3), 595-604.


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.


Cite This Article

M. Cao, S. Zhou and H. Gao, "A recommendation approach based on product attribute reviews: improved collaborative filtering considering the sentiment polarity," Intelligent Automation & Soft Computing, vol. 25, no.3, pp. 595–604, 2019.

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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