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http://dx.doi.org/10.15207/JKCS.2022.13.03.203

Design of customized product recommendation model on correlation analysis when using electronic commerce  

Yang, MingFei (Management Information System, Chungbuk National University)
Park, Kiyong (Department of Big Data, Chungbuk National University)
Choi, Sang-Hyun (Management Information System, Department of Big Data, Chungbuk National University)
Publication Information
Journal of the Korea Convergence Society / v.13, no.3, 2022 , pp. 203-216 More about this Journal
Abstract
In the recent business environment, purchase patterns are changing around the influence of COVID-19 and the online market. This study analyzed cluster and correlation analysis based on purchase and product information. The cluster analysis of new methods was attempted by creating customer, product, and cross-bonding clusters. The cross-bonding cluster analysis was performed based on the results of each cluster analysis. As a result of the correlation analysis, it was analyzed that more association rules were derived from a cross-bonding cluster, and the overlap rate was less. The cross-bonding cluster was found to be highly efficient. The cross-bonding cluster is the most suitable model for recommending products according to customer needs. The cross-bonding cluster model can save time and provide useful information to consumers. It is expected to bring positive effects such as increasing sales for the company.
Keywords
Online; Electronic commerce; Product recommendation model; Cluster analysis; Correlation analysis;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
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