Latent subject-centered modeling of collaborative tagging: An application in social search

Jing Peng, Daniel D. Zeng, Zan Huang

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

Collaborative tagging or social bookmarking is a main component of Web 2.0 systems and has been widely recognized as one of the key technologies underpinning next-generation knowledge management platforms. In this article, we propose a subject-centered model of collaborative tagging to account for the ternary cooccurrences involving users, items, and tags in such systems. Extending the well-established probabilistic latent semantic analysis theory for knowledge representation, our model maps the user, item, and tag entities into a common latent subject space that captures the "wisdom of the crowd" resulted from the collaborative tagging process. To put this model into action, we have developed a novel way to estimate the probabilistic subject- centeredmodel approximately in a highly efficient manner taking advantage of amatrix factorization method. Our empirical evaluation shows that our proposed approach delivers substantial performance improvement on the knowledge resource recommendation task over the state-of-the-art standard and tag-aware resource recommendation algorithms.

Original languageEnglish (US)
Article number15
JournalACM Transactions on Management Information Systems
Volume2
Issue number3
DOIs
StatePublished - Oct 1 2011

Keywords

  • Collaborative tagging
  • Item recommendation
  • Social search
  • Subject-centered modeling
  • Tag-based recommendation

ASJC Scopus subject areas

  • Management Information Systems
  • Computer Science(all)

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