Machine learning for user modeling

Webb, Geofferey I., Pazzani, Michael J. and Billsus, Daniel 2001, Machine learning for user modeling, User modeling and user-adapted interaction: the journal of personalization research, vol. 11, no. 1-2, pp. 19-29.

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Title Machine learning for user modeling
Author(s) Webb, Geofferey I.
Pazzani, Michael J.
Billsus, Daniel
Journal name User modeling and user-adapted interaction: the journal of personalization research
Volume number 11
Issue number 1-2
Start page 19
End page 29
Publisher Springer Netherlands
Place of publication Dordrecht, Netherlands
Publication date 2001-03
ISSN 0924-1868
1573-1391
Keyword(s) User modeling
Machine learning
Concept drift
Computational complexity
World wide web
Information agents
Summary At first blush, user modeling appears to be a prime candidate for straightforward application of standard machine learning techniques. Observations of the user's behavior can provide training examples that a machine learning system can use to form a model designed to predict future actions. However, user modeling poses a number of challenges for machine learning that have hindered its application in user modeling, including: the need for large data sets; the need for labeled data; concept drift; and computational complexity. This paper examines each of these issues and reviews approaches to resolving them.
Language eng
Field of Research 080110 Simulation and Modelling
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2001, Kluwer Academic Publishers
Persistent URL http://hdl.handle.net/10536/DRO/DU:30001054

Document type: Journal Article
Collection: School of Information Technology
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