Survey of data mining approaches to user modeling for adaptive hypermedia

Enrique Frias-Martinez, Sherry Y. Chen, Xiaohui Liu

Research output: Contribution to journalReview articlepeer-review

58 Scopus citations

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.

Original languageEnglish
Pages (from-to)734-749
Number of pages16
JournalIEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
Volume36
Issue number6
DOIs
StatePublished - Nov 2006

Keywords

  • Adaptive hypermedia (AH)
  • Data mining
  • Machine learning
  • User modeling (UM)

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