Open Access

ARTICLE

A K-means++ Based User Classification Method for Social E-commerce

Haoliang Cui¹, Shaozhang Niu¹, Keyue Li^1,*, Chengjie Shi², Shuai Shao³, Zhenguang Gao⁴

1 School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China
2 Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100088, China
3 China Information Technology Security Evaluation Center, Beijing, 100088, China
4 Department of Computer Science, Framingham State University, Framingham, MA, 01772, USA

* Corresponding Author: Keyue Li. Email:

Intelligent Automation & Soft Computing 2021, 28(1), 277-291. https://doi.org/10.32604/iasc.2021.016408

Received 01 January 2021; Accepted 02 February 2021; Issue published 17 March 2021

Abstract

At present, the research on the classification of e-commerce users is relatively mature, but with the rise of mobile social networks, the combination of social networks and e-commerce networks has become a trend and is developing rapidly. Traditional e-commerce user classification methods are not suitable for social e-commerce users. Therefore, based on the research on traditional e-commerce user classification methods, according to the characteristics of social e-commerce users, we improved data preprocessing and parameter tuning methods, and proposed a clustering method of social e-commerce users based on the K-means++ algorithm. The test on the actual data of social e-commerce users showed that the retention rates of users of various classes are significantly different, which express that the proposed method can classify social e-commerce users accurately.

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Citations