At first blush, user modeling appears to be a prime candidate for straight forward 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 labelled data; concept drift; and computational complexity. This paper examines each of these issues and reviews approaches to resolving them.
%0 Journal Article
%1 WebbPazzaniBillsus01
%A Webb, G. I.
%A Pazzani, M. J.
%A Billsus, D.
%C Netherlands
%D 2001
%I Springer
%J User Modeling and User-Adapted Interaction
%K Based Feature Modeling Modeling, User
%P 19-20
%T Machine learning for user modeling
%V 11
%X At first blush, user modeling appears to be a prime candidate for straight forward 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 labelled data; concept drift; and computational complexity. This paper examines each of these issues and reviews approaches to resolving them.
@article{WebbPazzaniBillsus01,
abstract = {At first blush, user modeling appears to be a prime candidate for straight forward 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 labelled data; concept drift; and computational complexity. This paper examines each of these issues and reviews approaches to resolving them.},
added-at = {2016-03-20T05:42:04.000+0100},
address = {Netherlands},
audit-trail = {Link to pdf via UMUAI site. Also available at http://www.kluweronline.com/issn/0924-1868},
author = {Webb, G. I. and Pazzani, M. J. and Billsus, D.},
biburl = {https://www.bibsonomy.org/bibtex/27d70ba26940ddf376e3fa903b64eb48a/giwebb},
interhash = {036fce6e8d680e9cb675e062772b282f},
intrahash = {7d70ba26940ddf376e3fa903b64eb48a},
journal = {User Modeling and User-Adapted Interaction},
keywords = {Based Feature Modeling Modeling, User},
pages = {19-20},
publisher = {Springer},
timestamp = {2016-03-20T05:42:04.000+0100},
title = {Machine learning for user modeling},
volume = 11,
year = 2001
}