Abstract
This study suggests a recommendation agent system that the user can optimally sort out incoming email messages according to category. The system is an effective way to manage ever-increasing email documents. For more accurate classification, the Bayesian learning algorithm using dynamic threshold has been applied. As a solution to the problem of erroneous classification, we suggest the following two approaches: First is the algorithmic approach that improves the accuracy of the classification by using dynamic threshold of the existing Bayesian algorithm. Second is the methodological approach using recommendation agent that the user, not the auto-sort, can make the final decision. In addition, major modules are based on rule filtering components for scalability and reusability.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Pazzani, M., Billsus, D.: Learning and Revising User Profiles: The Identification of Interesting Web sites. Machin Learning 27, 313–331 (1997)
Balabanovic, M., Shoham, Y.: Fab: Content-Based Collaborative Recommendation. CACM 40(3), 66–72 (1997)
Hill, W., Stead, L., Rosenstein, M., Furnas, G.: Recommending and Evaluating Choices in a Virtual Community of Use. In: CHI 1995, pp. 194–201 (1995)
Ian, H., Frank, E.: Data Mining. Morgan Kaufmann Publishers. Inc., San Francisco (2000)
Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Addison-Wesley, Reading (1999)
Cohen, W.W.: Learning Rules that Classify E-Mail. In: AAAI Spring symposium on Machine Learning in Information Access, pp. 18–25 (1996)
Frye, c.: Understanding Components. Andersen Consulting Knowledge Xchange (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jeong, OR., Cho, DS. (2004). Component-Based Recommendation Agent System for Efficient Email Inbox Management. In: Zhang, J., He, JH., Fu, Y. (eds) Computational and Information Science. CIS 2004. Lecture Notes in Computer Science, vol 3314. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30497-5_126
Download citation
DOI: https://doi.org/10.1007/978-3-540-30497-5_126
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24127-0
Online ISBN: 978-3-540-30497-5
eBook Packages: Computer ScienceComputer Science (R0)