@Article{iasc.2021.016408,
AUTHOR = {Haoliang Cui, Shaozhang Niu, Keyue Li, Chengjie Shi, Shuai Shao, Zhenguang Gao},
TITLE = {A K-means++ Based User Classification Method for Social E-commerce},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {28},
YEAR = {2021},
NUMBER = {1},
PAGES = {277--291},
URL = {http://www.techscience.com/iasc/v28n1/41773},
ISSN = {2326-005X},
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.},
DOI = {10.32604/iasc.2021.016408}
}