Affiliations:
University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan, Guangdong, China
Correspondence:
[*]
Corresponding author. Bin Yin, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan, Guangdong, 528400, China. E-mail: nodao65@163.com.
Abstract: The recommendation system is an important means to solve the “information overload” of e-commerce today. Consumer psychology believes that consumer psychology dominates consumer behavior, and consumer behavior is the external manifestation of consumer psychology. Therefore, the personalized recommendation algorithm of user consumption psychology is studied based on the specific perspective of local group-buying e-commerce. By constructing a user social relationship network, the personalized recommendation algorithm is evaluated and the final recommendation result is obtained. A personalized recommendation model is proposed based on multi-dimensional space, which is compared with the existing personalized recommendation model. The simulation results show that the improved collaborative filtering recommendation method has a large recall rate and accuracy during the daytime. And F value; when the number of recommended results is small at night, the traditional recommendation method has a slightly larger recall rate, accuracy rate and F value, but as the number of recommended results increases, the recommended effects decrease. In general, the proposed method of the recommended algorithm has a good effect. The method proposed in this paper can improve the accuracy of recommendation and partially eliminate the cold start problem of users, which has certain enlightenment for the expansion of personalized recommendation algorithm and the improvement of e-commerce user management.
Keywords: Group purchase, e-commerce users, consumer psychology
