Group User Models for Personalized Hyperlink Recommendations

  • Authors:
  • Johan Bollen

  • Affiliations:
  • -

  • Venue:
  • AH '00 Proceedings of the International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
  • Year:
  • 2000

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Abstract

This paper presents a system that combines adaptive hypertext linking based on group link preferences with an implicit navigation-based mechanism for personalized link recommendations. A methodology using three Hebbian-style learning rules changes hyperlink weights according to users' overlapping navigation paths and causes a hypertext system's link structure to converge to a valid group user model. A spreading activation recommendation system generates navigation path based recommendations for individual users. Both systems are linked, thereby combining both personal user interests and established group link preferences. An on-line application for the Los Alamos National Laboratory Research Library is presented.