Abstract
Email is one of the most successful computer applications in the Internet, and email-spam is also the biggest problem for users, preventing them to quickly process the important emails in a shortest time. In this paper, we propose an email recommender system using user actions and statistical methods. Instead of a two-class classification with Spam and Ham, we treat the problem as a multi-class classification in which each class is a recommendation action from user to an email. The most common actions are: reply, read and delete. An experiment is also conducted to test the framework, using Naïve Bayesian classifier and different threshold to evaluate the relations between number of features and the performance. The experiment shows a promising result with good prediction accuracy.
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Ha, Q.M., Tran, Q.A., Luyen, T.T. (2012). Personalized Email Recommender System Based on User Actions. In: Bui, L.T., Ong, Y.S., Hoai, N.X., Ishibuchi, H., Suganthan, P.N. (eds) Simulated Evolution and Learning. SEAL 2012. Lecture Notes in Computer Science, vol 7673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34859-4_28
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DOI: https://doi.org/10.1007/978-3-642-34859-4_28
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