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Group User Models for Personalized Hyperlink Recommendations

Group User Models for Personalized Hyperlink Recommendations,10.1007/3-540-44595-1_5,Johan Bollen

Group User Models for Personalized Hyperlink Recommendations   (Citations: 19)
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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.
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