Design of Adaptive Electronic Commerce Agents Using Machine Learning Techniques

KIPS Journal B (2001 ~ 2012) , Vol. 9B, No.6, pp.775-782, June 2002
10.3745/KIPSTB.2002.9B.6.775, Full Text

Abstract

As electronic commerce systems have been widely used, the necessity of adaptive e-commerce agent systems has been increased. These kinds of agents can monitor customer's purchasing behaviors, cluster them in similar categories, and induce customer's preference from each category. In order to implement our adaptive e-commerce agent system, we focus on following 3 components - the monitor agent which can monitor customer's browsing/purchasing data and abstract them, the conceptual cluster agent which cluster customer's abstract data, and the customer profile agent which generate profile from cluster. In order to infer more accurate customer's preference, we propose a 2 layered structure consisting of conceptual cluster and inductive profile generator. Many systems have been suffered from errors in deriving user profiles by using a single structure. However, our proposed 2 layered structure enables us to improve the quality of user profile by clustering user purchasing behavior in advance. This approach enables us to build more user adaptive e-commerce system according to user purchasing behavior.


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Cite this paper

[KIPS Transactions Style]
H. J. Baek and Y. T. Park, "Design of Adaptive Electronic Commerce Agents Using Machine Learning Techniques," KIPS Journal B (2001 ~ 2012) , Vol.9B, No.6, pp.775-782, 2002, DOI: 10.3745/KIPSTB.2002.9B.6.775.

[IEEE Style]
Hey Jung Baek and Young Tack Park, "Design of Adaptive Electronic Commerce Agents Using Machine Learning Techniques," KIPS Journal B (2001 ~ 2012) , vol. 9B, no. 6, pp. 775-782, 2002. DOI: 10.3745/KIPSTB.2002.9B.6.775.

[ACM Style]
Baek, H. J. and Park, Y. T. 2002. Design of Adaptive Electronic Commerce Agents Using Machine Learning Techniques. KIPS Journal B (2001 ~ 2012) , 9B, 6, (2002), 775-782. DOI: 10.3745/KIPSTB.2002.9B.6.775.