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Several machine learning approaches have been proposed in the literature to automatically learn user interests for information filtering. However, many of them are ill-equipped to deal with changes in user interests that may occur due to changes in the user's personal and proikssionai situations. If undetected over a long time, such changes may cause significant degradation in the filtering performance and user satisfaction during the period of non-detection. In this paper, we present adoi:10.1145/243199.243279 dblp:conf/sigir/LamMMP96 fatcat:agxegvm5tveltc62l2acgrtpne