Mining latent user community for tag-based and content-based search in social media

Haoran XIE, Qing LI, Xudong MAO, Xiaodong LI, Yi CAI*, Qianru ZHENG

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

35 Citations (Scopus)


In recent years, there has been a proliferation of collaborative tagging systems in Web 2.0 communities.With the increasingly large amount of social data, how to manage and organize them becomes an important and crucial problem for folksonomy applications. To better understand and meet users' needs, multimedia resources can be organized or indexed from these user perspectives; it is thus important to find latent user communities for social media applications. In this paper, we propose the mechanism of augmented folksonomy graph (AFG) to incorporate multi-Faceted relations in social media, along with a novel density-Based clustering method to discover latent user community fromAFGby combining contents and tags of multimedia resources.To evaluate the proposed method, we conduct experiments on a public dataset, the empirical results of which show that our approach outperforms baseline ones in terms of tag-Based and content-Based personalized search.

Original languageEnglish
Pages (from-to)1415-1430
Number of pages16
JournalComputer Journal
Issue number9
Early online date2 Apr 2014
Publication statusPublished - Sept 2014
Externally publishedYes


  • Personalized search
  • Social media
  • User community
  • Web 2.0


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