Effective personalization is greatly demanded in highly heterogeneous and diverse e-commerce domain. In our approach we rely on the idea that an effective personalization technique has to be customized to meet the specific needs of every particular domain and deliver quality recommendations. With this in mind, we have combined Bayesian classification methods with association rule mining to model individual customer’s behavior. While Bayesian classifier is for effective customer profiles, rulesbased analysis works for both customer and non-customer objectives, such as reducing over-stocked items. This paper also presents a comparative analysis of the existing personalization techniques for the improvement of a distributed online customer care application. In this paper, we have successfully demonstrated on the example of the SprintPCS customer care domain that our approach is an efficient recommendation model for the online customer care.
Published Date: May 2004
Registration: ISBN 978-1-57735-201-3
Copyright: Published by The AAAI Press, Menlo Park, California.