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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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