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Neighborhood-Based Tag Prediction

  • Adriana Budura
  • Sebastian Michel
  • Philippe Cudré-Mauroux
  • Karl Aberer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5554)

Abstract

We consider the problem of tag prediction in collaborative tagging systems where users share and annotate resources on the Web. We put forward HAMLET, a novel approach to automatically propagate tags along the edges of a graph which relates similar documents. We identify the core principles underlying tag propagation for which we derive suitable scoring models combined in one overall ranking formula. Leveraging these scores, we present an efficient top-k tag selection algorithm that infers additional tags by carefully inspecting neighbors in the document graph. Experiments using real-world data demonstrate the viability of our approach in large-scale environments where tags are scarce.

Keywords

Document Graph Place Semantic Initial Document Index List Social Annotation 
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 2009

Authors and Affiliations

  • Adriana Budura
    • 1
  • Sebastian Michel
    • 1
  • Philippe Cudré-Mauroux
    • 2
  • Karl Aberer
    • 1
  1. 1.Ecole Polytechnique Fédérale de Lausanne (EPFL)Switzerland
  2. 2.MITUSA

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