2001 MachineLearningForUserModeling

• (Webb et al., 2001) ⇒ Geoffrey I. Webb, Michael J. Pazzani, Daniel Billsus. (2001). “Machine Learning for User Modeling.” In: Journal for User Modeling and User-Adapted Interaction, 11(1-2) doi:10.1023/A:1011117102175

Subject Headings: User Modeling.

Quotes

Author Keywords

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

Abstract

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.

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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.

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References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2001 MachineLearningForUserModeling	Geoffrey I. Webb Michael J. Pazzani Daniel Billsus			Machine Learning for User Modeling		Journal for User Modeling and User-Adapted Interaction	http://www.fxpal.com/people/billsus/pubs/webb.pdf	10.1023/A:1011117102175		2001