2001 MachineLearningForUserModeling

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Subject Headings: User Modeling.


Cited By


Author Keywords

user modeling, machine learning, concept drift, computational complexity, World Wide Web, information agents


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.

6. Computational Complexity

The current ML for UM resurgence has witnessed tremendous research activity. In contrast, the field still has a dearth of fielded applications. The resulting difference between research interest and commercially deployed systems is especially apparent in the field of Internet-based applications.



 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2001 MachineLearningForUserModelingGeoffrey I. Webb
Michael J. Pazzani
Daniel Billsus
Machine Learning for User ModelingJournal for User Modeling and User-Adapted Interactionhttp://www.fxpal.com/people/billsus/pubs/webb.pdf10.1023/A:10111171021752001