TagClusters: Semantic Aggregation of Collaborative Tags beyond TagClouds

  • Authors:

  • Ya-Xi Chen;Rodrigo Santamaría;Andreas Butz;Roberto Therón

  • Affiliations:

  • Media Informatics, University of Munich, Munich, Germany 80333;Department of Informatics and Automatics, University of Salamanca, Salamanca, Spain CP 37008;Media Informatics, University of Munich, Munich, Germany 80333;Department of Informatics and Automatics, University of Salamanca, Salamanca, Spain CP 37008

  • Venue:
  • SG '09 Proceedings of the 10th International Symposium on Smart Graphics
  • Year:
  • 2009

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Abstract

TagClouds is a popular visualization for the collaborative tags. However it has some instinct problems such as linguistic issues, high semantic density and poor understanding of hierarchical structure and semantic relation between tags. In this paper we investigate the ways to support semantic understanding of collaborative tags and propose an improved visualization named TagClusters. Based on the semantic analysis of the collaborative tags in Last.fm, the semantic similar tags are clustered into different groups and the visual distance represents the semantic similarity between tags, and thus the visualization offers a better semantic understanding of collaborative tags. A comparative evaluation is conducted with TagClouds and TagClusters based on the same tags collection. The results indicate that TagClusters has advantages in supporting efficient browsing, searching, impression formation and matching. In the future work, we will explore the possibilities of supporting tag recom-mendation and tag-based Music Retrieval based on TagClusters.