一种社交电商平台的用户行为分析方法  

User Behavior Analysis Method of Social E-Commerce Platform

作　　者：高昀 牛少彰[1] GAO Yun;NIU Shao-zhang(Computer Science School,Beijing University of Posts and Telecommunications,Beijing 100876,China)

机构地区：[1]北京邮电大学计算机学院,北京100876

出　　处：《新一代信息技术》2021年第9期16-23,共8页New Generation of Information Technology

基　　金：国家自然科学基金(项目编号:61370195,61070207)。

摘　　要：随着近几年互联网经济以及社交化平台的蓬勃发展和兴起,社交电商群体越发活跃在大众的眼前,在中国的经济社会中占有越来越重要的位置,对社交电商这个新兴的群体进行用户行为分析对于经济社会发展来说具有重要意义。本文借助大数据挖掘技术,对社交电商的行为进行聚类分析,将改进后的Canopy算法结合K-means聚类算法运用在社交电商的功能访问数据上。实验证明,该算法能够有效地根据社交电商特征进行聚类分析,划分社交电商群体,为社交电商提供个性化服务方面提供理论依据。In recent years,with the flourishing development and the rise of the Internet economy and social platforms,social e-commerce community are more and more active in the eyes of the public,and occupy an increasingly important position in China’s economic society.It is so important for the economic and social development to analyze the user behavior of the social e-commerce community.With the help of big data mining technology,this paper analyzes the behavior of the social e-commerce,and applies K-means clustering algorithm with the improved canopy algorithm to the function access data of the social e-commerce.The experimental results show that the algorithm can effectively cluster the social e-commerce community according to the characteristics of the social e-commerce,and provide the theoretical basis for providing personalized services for the social e-commerce.

关 键 词：大数据挖掘 社交电商 用户行为 聚类分析 K-MEANS 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]