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Scaling Pair-Wise Similarity-Based Algorithms in Tagging Spaces

  • Damir Vandic
  • Flavius Frasincar
  • Frederik Hogenboom
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7387)

Abstract

Users of Web tag spaces, e.g., Flickr, find it difficult to get adequate search results due to syntactic and semantic tag variations. In most approaches that address this problem, the cosine similarity between tags plays a major role. However, the use of this similarity introduces a scalability problem as the number of similarities that need to be computed grows quadratically with the number of tags. In this paper, we propose a novel algorithm that filters insignificant cosine similarities in linear time complexity with respect to the number of tags. Our approach shows a significant reduction in the number of calculations, which makes it possible to process larger tag data sets than ever before. To evaluate our approach, we used a data set containing 51 million pictures and 112 million tag annotations from Flickr.

Keywords

Input Vector Parameter Combination Cosine Similarity Scalability Issue Inverted Index 
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 2012

Authors and Affiliations

  • Damir Vandic
    • 1
  • Flavius Frasincar
    • 1
  • Frederik Hogenboom
    • 1
  1. 1.Erasmus University RotterdamRotterdamThe Netherlands

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