Detection of Shifts in User Interests for Personalized Information Filtering.

Wai Lam, Snehasis Mukhopadhyay, Javed Mostafa, Mathew J. Palakal: Detection of Shifts in User Interests for Personalized Information Filtering. SIGIR 1996: 317-325
  author    = {Wai Lam and
               Snehasis Mukhopadhyay and
               Javed Mostafa and
               Mathew J. Palakal},
  title     = {Detection of Shifts in User Interests for Personalized Information
  booktitle = {SIGIR},
  year      = {1996},
  pages     = {317-325},
  ee        = {db/conf/sigir/LamMMP96.html},
  bibsource = {DBLP,}


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 professional 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 two-level learning approach to cope with such non-stationary user interests. While the lower level consists of a standard convergence-type machine learning algorithm, the higher level uses Bayesian 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.

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Hans-Peter Frei, Donna Harman, Peter Schäuble, Ross Wilkinson (Eds.): Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'96, August 18-22, 1996, Zurich, Switzerland (Special Issue of the SIGIR Forum). ACM 1996, ISBN 0-89791-792-8
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