Research on Consumer Psychology Modeling and Intelligent Recommendation of E-commerce Users
Haijiang Huang, Lizhu Ying · 2021
To better understand the user’s behavior, depict the user’s psychological portrait, and provide intelligent services for e-commerce platform and users, this paper proposes a recommendation method based on users’ perceptual needs. In order to solve the problem of inaccurate recommendation results when the user’s awareness changes, a multi-attitude personalized recommendation algorithm is proposed, and the Bayesian network technology is used to establish the user’s network consumption psychological model. Then, according to the consumption behavior and psychological characteristics of different users, we can make personalized marketing plans. Finally, an example is provided to illustrate the rationality of the recommendation method from the perspective of the accuracy of the recommendation results, which provides a reference for analyzing the consumer psychology of users in e-commerce.