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

Haoliang Cui1, Shaozhang Niu1, Keyue Li1,*, 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.


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.


Cite This Article

H. Cui, S. Niu, K. Li, C. Shi, S. Shao et al., "A k-means++ based user classification method for social e-commerce," Intelligent Automation & Soft Computing, vol. 28, no.1, pp. 277–291, 2021.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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