Abstract
As the wide use of online business transactions, the volume of product information that needs to be managed in a system has become drastically large, and the classification task of such data has become highly complex. The heterogeneity among competing standard classification schemes makes the problem only harder. However, the classification task is an indispensable part for successful e-commerce applications. In this paper, we present an automated approach for e-catalog classification. We extend the Naïve Bayes Classifier to make use of the structural characteristics of e-catalogs. We show how we can improve the accuracy of classification when appropriate characteristics of e-catalogs are utilized. Effectiveness of the proposed methods is validated through experiments.
This work was supported by the Ministry of Information & Communications, Korea, under the Information Technology Research Center (ITRC) Support Program.
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Kim, Yg., Lee, T., Chun, J., Lee, Sg. (2006). Modified Naïve Bayes Classifier for E-Catalog Classification. In: Lee, J., Shim, J., Lee, Sg., Bussler, C., Shim, S. (eds) Data Engineering Issues in E-Commerce and Services. DEECS 2006. Lecture Notes in Computer Science, vol 4055. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11780397_20
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DOI: https://doi.org/10.1007/11780397_20
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