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Resource Recommendation in Collaborative Tagging Applications

  • Jonathan Gemmell
  • Thomas Schimoler
  • Bamshad Mobasher
  • Robin Burke
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 61)

Abstract

Collaborative tagging applications enable users to annotate online resources with user-generated keywords. The collection of these annotations and the way they connect users and resources produce a rich information space for users to explore. However the size, complexity and chaotic structure of these systems hamper users as they search for information. Recommenders can assist the user by suggesting resources, tags or even other users. Previous work has demonstrated that an integrative approach which exploits all three dimensions of the data (users, resources, tags) produce superior results in tag recommendation. We extend this integrative philosophy to resource recommendation. Specifically, we propose an approach for designing weighted linear hybrid resource recommenders. Through extensive experimentation on two large real world datasets, we show that the hybrid recommenders surpass the effectiveness of their constituent components while inheriting their simplicity, computational efficiency and explanatory capacity. We further introduce the notion of information channels which describe the interaction of the three dimensions. Information channels can be used to explain the effectiveness of individual recommenders or explain the relative contribution of components in the hybrid recommender.

Keywords

Collaborative Tagging Information Channel Hybrid Recommender 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jonathan Gemmell
    • 1
  • Thomas Schimoler
    • 1
  • Bamshad Mobasher
    • 1
  • Robin Burke
    • 1
  1. 1.Center for Web Intelligence, School of ComputingDePaul UniversityChicagoUSA

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