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Detection of shifts in user interests for personalized information filtering
1996
Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '96
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 a
doi:10.1145/243199.243279
dblp:conf/sigir/LamMMP96
fatcat:agxegvm5tveltc62l2acgrtpne