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Tag-Based Resource Recommendation in Social Annotation Applications

  • Jonathan Gemmell
  • Thomas Schimoler
  • Bamshad Mobasher
  • Robin Burke
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6787)

Abstract

Social annotation systems enable the organization of online resources with user-defined keywords. The size and complexity of these systems make them excellent platforms for the application of recommender systems, which can provide personalized views of complex information spaces. Many researchers have concentrated on the important problem of tag recommendation. Less attention has been paid to the recommendation of resources in the context of social annotation systems. In this paper, we examine the specific case of tag-based resource recommendation and propose a linear-weighted hybrid for the task. Using six real world datasets, we show that our algorithm is more effective than other more mathematically complex techniques.

Keywords

Recommender System Cosine Similarity Collaborative Filter Recommendation Algorithm Tensor Factorization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

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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