Research on recommendation algorithm combining user comments and rating information
ZHNAG Shu1， LI Hui2，3*， SHI Jun2， WANG Chengqing2
(1 School of Business, Jiangsu Ocean University, Lianyaungang 222005, Jiangsu, China; 2 School of Computer Engineering, Jiangsu Ocean University, Lianyungang 222005， Jiangsu, China;3 Jiangsu Institute of Marine Resources Development,Lianyungang 221005, Jiangsu, China)
Abstract:
In order to improve the accuracy of the recommendation system to recommend new products to users, it is necessary to discover the hidden preferences of each customer and the performance of each product. User feedback techniques were often used to discover potential characteristics and user dimensions of a product.A recommendation model is presented that combines potential factors in user ratings with potential topics in reviews. The model enables more accurate scoring predictions by utilizing the information presented in the review text, and is particularly useful for scoring predictions for new products and new users. Through the verification experiments on the public dataset, it is proved that the model has a significant improvement in performance compared with the traditional recommendation system.
KeyWords:
ranking; credit; comment; predict; LDA; recommend
