A Consumer Behavior Prediction Model Based on Multivariate Real-Time Sequence Analysis | Zendy

Lin Guo | Zendy; Ben Zhang | Zendy; Xin Zhao | Zendy

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A Consumer Behavior Prediction Model Based on Multivariate Real-Time Sequence Analysis

Author(s) -

Lin Guo,

Ben Zhang,

Xin Zhao

Publication year - 2021

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2021/6688750

Subject(s) - computer science , sequence (biology) , multivariate statistics , payment , consumption (sociology) , consumer behaviour , big data , the internet , sampling (signal processing) , data mining , time sequence , time series , artificial intelligence , machine learning , advertising , world wide web , social science , genetics , filter (signal processing) , sociology , business , computer vision , biology

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.

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