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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

35 Scopus Citations
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Author(s)

  • Haoran Xie
  • Xiaodong Li
  • Yi Cai
  • Qianru Zheng

Detail(s)

Original languageEnglish
Pages (from-to)1415-1430
Journal / PublicationComputer Journal
Volume57
Issue number9
Online published2 Apr 2014
Publication statusPublished - Sep 2014

Abstract

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. © The British Computer Society 2014.

Research Area(s)

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

Citation Format(s)

Mining latent user community for tag-Based and content-Based search in social media. / Xie, Haoran; Li, Qing; Mao, Xudong; Li, Xiaodong; Cai, Yi; Zheng, Qianru.

In: Computer Journal, Vol. 57, No. 9, 09.2014, p. 1415-1430.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review