Automated user modeling for personalized digital libraries

E. Frias-Martinez, G. Magoulas, S. Chen, R. Macredie

Research output: Contribution to journalArticlepeer-review

50 Scopus citations

Abstract

Digital libraries (DLs) have become one of the most typical ways of accessing any kind of digitalized information. Due to this key role, users welcome any improvements on the services they receive from DLs. One trend used to improve digital services is through personalization. Up to now, the most common approach for personalization in DLs has been user driven. Nevertheless, the design of efficient personalized services has to be done, at least in part, in an automatic way. In this context, machine learning techniques automate the process of constructing user models. This paper proposes a new approach to construct DLs that satisfy a user's necessity for information: Adaptive DLs, libraries that automatically learn user preferences and goals and personalize their interaction using this information.

Original languageEnglish
Pages (from-to)234-248
Number of pages15
JournalInternational Journal of Information Management
Volume26
Issue number3
DOIs
StatePublished - Jun 2006

Keywords

  • Adaptive library services
  • Digital libraries
  • Personalization
  • User modeling

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