Detection of shifts in user interests for personalized information filtering

W. Lam, S. Mukhopadhyay, J. Mostafa, M. Palakal
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
more » ... l learning approach to cope with such nonstationary user interests. While the lower level consists of a standard convergence-type machine learning algorithm, the higher level uses Bayesiart analysis of the user provided relevance feedback to detect shifts in user interests. Once such a shift is detected, the lower-level learning algorithm is suitably reinitialized to quickly adapt to the new user profile. Experimental results with simulated users are presented to demonstrate the feasibility of the approach.
doi:10.1145/243199.243279 dblp:conf/sigir/LamMMP96 fatcat:agxegvm5tveltc62l2acgrtpne