Abstract

In this paper we presentPEA,aPersonal Email Assistant, which lters incoming emails and ranks them according to their relevance. We provide tools for the acquisition of individual user models, which may consist of several pro les to map various interest domains of the user. In order to respond promptly to the shifts of interests of a user, we apply evolutionary algorithms to support an adaptive environment that constantly adjusts the user model to improve the quality of relevance assessment. As second adaptive component wemake use of a monitoring module that records all activities of the user. By means of a classi er system we model the behavior of the user to predict future actions, which results rst in suggestions to the user and later in automatically performed tasks. Additional features of the system include the segmentation of lengthy emails, e cient treatment of duplicate or new versions of messages, cross-language ltering, and the extraction of relevant information by using templates learned from examples. 1

Citations