A personalized recommendation agent system for e-mail document classification

Abstract

Overload of information due to rapidly developing Internet and increases of e-mails are inconvenience for all Netizens now. Many existing recommendation systems or text classification using personalization techniques are mostly focused on recommending the products for the commercial purposes or web documents. This study aims to apply these application categories to e-mail more necessary to users. Moreover, this study tries to improve the accuracy as eliminating the limits of misclassification that can be key in classifying e-mails by category and deleting Spam mails. This paper suggests a Personalized Recommendation Agent System (PRAS) recommending the relevant category to enable users directly to manage the optimum classification when new e-mail is received as the effective method for e-mail management. While the existing Bayesian Learning Algorithm mostly uses the fixed threshold, this study proves to improve the satisfaction of users as increasing the accuracy by changing the fixed threshold to the dynamic threshold. © Springer-Verlag Berlin Heidelberg 2004.