Automated user modeling for personalized digital libraries

  • Authors:

  • E. Frias-Martinez;G. Magoulas;S. Chen;R. Macredie

  • Affiliations:

  • Department of Information Systems & Computing, Brunel University, Uxbridge, Middlesex UB8 3PH, UK;School of Computer Science & Information Systems, Birkbeck College, University of London, Malet Street, London WC1E 7HX, UK;Department of Information Systems & Computing, Brunel University, Uxbridge, Middlesex UB8 3PH, UK;Department of Information Systems & Computing, Brunel University, Uxbridge, Middlesex UB8 3PH, UK

  • Venue:
  • International Journal of Information Management: The Journal for Information Professionals
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

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.