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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/3200

Title: Survey of data mining approaches to user modeling for adaptive hypermedia
Authors: Frias-Martinez, E
Chen, SY
Liu, X
Keywords: Adaptive hypermedia (AH)
Data Mining
machine learning
user modeling (UM)
Publication Date: 2006
Citation: Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on. 36 (6): 734-749, Nov 2006
Abstract: The ability of an adaptive hypermedia system to create tailored environments depends mainly on the amount and accuracy of information stored in each user model. Some of the difficulties that user modeling faces are the amount of data available to create user models, the adequacy of the data, the noise within that data, and the necessity of capturing the imprecise nature of human behavior. Data mining and machine learning techniques have the ability to handle large amounts of data and to process uncertainty. These characteristics make these techniques suitable for automatic generation of user models that simulate human decision making. This paper surveys different data mining techniques that can be used to efficiently and accurately capture user behavior. The paper also presents guidelines that show which techniques may be used more efficiently according to the task implemented by the application.
URI: http://bura.brunel.ac.uk/handle/2438/3200
ISSN: 1094-6977
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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