Abstract
We focus on user behaviour that most e-mail users browse the web. In this paper, we attempt to exploit user preference extracted from the behaviour in a spam filtering method. The method reduces troublesome maintenance of the filter, since it keeps track of user preference as ham words in background. The ham words are used to determine whether a received e-mail is a ham or not. The method can detect some spams which are hard to classify correctly by existing Bayesian filters. We show that a combination of a Bayesian filter and our method reduces the number of false negatives.
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