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Visitor Information System of Cross-Border E-Commerce Platform Based on Mobile Edge Computing
Author(s) -
Xiaheng Zhang,
Yong-Hua Cai,
Lin Xiao
Publication year - 2021
Publication title -
mobile information systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.346
H-Index - 34
eISSN - 1875-905X
pISSN - 1574-017X
DOI - 10.1155/2021/1687820
Subject(s) - visitor pattern , computer science , popularity , enhanced data rates for gsm evolution , e commerce , world wide web , mobile commerce , the internet , information system , mobile device , telecommunications , psychology , social psychology , electrical engineering , programming language , engineering
With the popularity of the Internet and the rapid development of e-commerce, online shopping has gradually become an indispensable part of people’s lives. Among them, the rise of cross-border e-commerce has become a focus of attention. The operation traces left by visitors during shopping on the e-commerce platform are stored in the database of the system, and the platform holds such a large amount of valuable data resources. How to unearth valuable content from these resources and apply them becomes very important. This article mainly introduces the research on the visitor information analysis system of the cross-border e-commerce platform based on mobile edge computing. This article first establishes the mobile edge computing framework based on the advantages of the mobile edge computing method and uses it to visit visitors in the visitor information analysis system. In the data filtering, secondly, the requirements of the visitor information analysis system of the cross-border e-commerce platform are analyzed to provide a design basis for the design of the visitor information system. Finally, the visitor information analysis based on the mobile edge algorithm is designed through the demand analysis of the system that has also been tested for visitor information analysis. The test pass rate is as high as 98%, and the accuracy rate of visitor information analysis reaches 80%.

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