- (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.
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.
|2001 MachineLearningForUserModeling||Geoffrey I. Webb|
Michael J. Pazzani
|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|
|Author||Geoffrey I. Webb +, Michael J. Pazzani + and Daniel Billsus +|
|journal||Journal for User Modeling and User-Adapted Interaction +|
|title||Machine Learning for User Modeling +|