A Consumer Behavior Prediction Model Based on Multivariate Real-Time Sequence Analysis. (26th May 2021)

• Record Type: Journal Article
• Authors: Guo, Lin; Zhang, Ben; Zhao, Xin
• Other Names: Balezentis Tomas Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2021(2021)
• Publication Date: 2021-05-26
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2021/6688750
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : With the rapid development of online finance and social networks, a large amount of behavioral data is stored on the Internet, which can fully reflect the shopping tendencies and habits of real users. Using big data to analyze consumer behavior is more scientific and accurate than the traditional sampling survey method. Internet consumption behavior data are time series data. Therefore, this paper proposes a method of analyzing behavioral sequence data, which learns personal consumption interests and habits, and finally predicts payment behavior. The experiments compare the execution effect of different algorithms on multiple databases and verify the feasibility and effectiveness of the proposed algorithm SeqLearn.