Using Electronic Platforms to Sell Mechanical Engineering Products

Vitiuk, Anna; Koliadenko, N. A.

doi:10.32983/2222-0712-2019-4-127-134

Cited by 1 publication

(1 citation statement)

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“…Identify the tag data information and behavior sequence information in the user's log according to the user's unique identifier, and then construct the user's access behavior [20,21], calculate the user's tag data similarity and behavior sequence similarity, and combine the two together. Evaluate the similarity of two users [22,23]. Similarity calculation can categorize the behavior information of visitors, and then perform category processing and analysis.…”

Section: Calculation Of Visitor Behavior Similaritymentioning

confidence: 99%

Visitor Information System of Cross-Border E-Commerce Platform Based on Mobile Edge Computing

Zhang

Cai

Xiao

2021

Mobile Information Systems

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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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