Demand-Driven Tag Recommendation

  • Guilherme Vale Menezes
  • Jussara M. Almeida
  • Fabiano Belém
  • Marcos André Gonçalves
  • Anísio Lacerda
  • Edleno Silva de Moura
  • Gisele L. Pappa
  • Adriano Veloso
  • Nivio Ziviani
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6322)

Abstract

Collaborative tagging allows users to assign arbitrary keywords (or tags) describing the content of objects, which facilitates navigation and improves searching without dependence on pre-configured categories. In large-scale tag-based systems, tag recommendation services can assist a user in the assignment of tags to objects and help consolidate the vocabulary of tags across users. A promising approach for tag recommendation is to exploit the co-occurrence of tags. However, these methods are challenged by the huge size of the tag vocabulary, either because (1) the computational complexity may increase exponentially with the number of tags or (2) the score associated with each tag may become distorted since different tags may operate in different scales and the scores are not directly comparable. In this paper we propose a novel method that recommends tags on a demand-driven basis according to an initial set of tags applied to an object. It reduces the space of possible solutions, so that its complexity increases polynomially with the size of the tag vocabulary. Further, the score of each tag is calibrated using an entropy minimization approach which corrects possible distortions and provides more precise recommendations. We conducted a systematic evaluation of the proposed method using three types of media: audio, bookmarks and video. The experimental results show that the proposed method is fast and boosts recommendation quality on different experimental scenarios. For instance, in the case of a popular audio site it provides improvements in precision (p@5) ranging from 6.4% to 46.7% (depending on the number of tags given as input), outperforming a recently proposed co-occurrence based tag recommendation method.

Keywords

Association Rule Latent Dirichlet Allocation Mean Reciprocal Rank Recommendation Performance Extract Association Rule 
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 2010

Authors and Affiliations

  • Guilherme Vale Menezes
    • 1
  • Jussara M. Almeida
    • 1
  • Fabiano Belém
    • 1
  • Marcos André Gonçalves
    • 1
  • Anísio Lacerda
    • 1
  • Edleno Silva de Moura
    • 2
  • Gisele L. Pappa
    • 1
  • Adriano Veloso
    • 1
  • Nivio Ziviani
    • 1
  1. 1.Universidade Federal de Minas GeraisBelo HorizonteBrazil
  2. 2.Universidade Federal do AmazonasManausBrazil

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