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, doi: 10.1023/A:1011117102175.

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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
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
DOI 10.1023/A:1011117102175
Field of Research 080110 Simulation and Modelling
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2001, Kluwer Academic Publishers
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Document type: Journal Article
Collection: School of Information Technology
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