SnoopyTagging: recommending contextualized tags to increase the quality and quantity of meta-information

  • Authors:
  • Wolfgang Gassler;Eva Zangerle;Martin Bürgler;Günther Specht

  • Affiliations:
  • University of Innsbruck, Innsbruck, Austria;University of Innsbruck, Innsbruck, Austria;University of Innsbruck, Innsbruck, Austria;University of Innsbruck, Innsbruck, Austria

  • Venue:
  • Proceedings of the 21st international conference companion on World Wide Web
  • Year:
  • 2012

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Abstract

Current mass-collaboration platforms use tags to annotate and categorize resources enabling eective search capabilities. However, as tags are freely chosen keywords, the resulting tag vocabulary is very heterogeneous. Another shortcoming of simple tags is that they do not allow for a specification of context to create meaningful metadata. In this paper we present the SnoopyTagging approach which supports the user in the process of creating contextualized tags while at the same time decreasing the heterogeneity of the tag vocabulary by facilitating intelligent self-learning recommendation algorithms.