Open Access
ARTICLE
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. 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.
Keywords
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.