JACIII Vol.23 No.5 pp. 864-873
doi: 10.20965/jaciii.2019.p0864


Study on Collaborative Filtering Recommendation Model Fusing User Reviews

Heyong Wang, Ming Hong, and Jinjiong Lan

Department of E-Commerce, School of Economics and Commerce, South China University of Technology
B10 South China University of Technology, Panyu District, Guangzhou, Guangdong 510006, China

August 10, 2018
April 1, 2019
September 20, 2019
collaborative filtering, user reviews, topic model, similarity fusion

The traditional collaborative filtering model suffers from high-dimensional sparse user rating information and ignores user preference information contained in user reviews. To address the problem, this paper proposes a new collaborative filtering model UL_SAM (UBCF_LDA_SIMILAR_ADD_MEAN) which integrates topic model with user-based collaborative filtering model. UL_SAM extracts user preference information from user reviews through topic model and then fuses user preference information with user rating information by similarity fusion method to create fusion information. UL_SAM creates collaborative filtering recommendations according to fusion information. It is the advantage of UL_SAM on improving recommendation effectiveness that UL_SAM enriches information for collaborative recommendation by integrating user preference with user rating information. Experimental results of two public datasets demonstrate significant improvement on recommendation effectiveness in our model.

Cite this article as:
H. Wang, M. Hong, and J. Lan, “Study on Collaborative Filtering Recommendation Model Fusing User Reviews,” J. Adv. Comput. Intell. Intell. Inform., Vol.23 No.5, pp. 864-873, 2019.
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