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.
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Cited By
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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 |