@Article{2019.100000114,
AUTHOR = {Min Cao, Sijing Zhou, Honghao Gao},
TITLE = {A Recommendation Approach Based on Product Attribute Reviews:  Improved Collaborative Filtering Considering the Sentiment Polarity},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {25},
YEAR = {2019},
NUMBER = {3},
PAGES = {595--604},
URL = {http://www.techscience.com/iasc/v25n3/39688},
ISSN = {2326-005X},
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.},
DOI = {10.31209/2019.100000114}
}