Journal Article

Mining Latent User Community for Tag-Based and Content-Based Search in Social Media

Haoran Xie, Qing Li, Xudong Mao, Xiaodong Li, Yi Cai and Qianru Zheng

in The Computer Journal

Volume 57, issue 9, pages 1415-1430
Published in print September 2014 | ISSN: 0010-4620
Published online April 2014 | e-ISSN: 1460-2067 | DOI: https://dx.doi.org/10.1093/comjnl/bxu022
Mining Latent User Community for Tag-Based and Content-Based Search in Social Media

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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 from AFG by 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.

Keywords: social media; personalized search; user community; Web 2.0

Journal Article.  0 words. 

Subjects: Computer Science

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