Community-aware user profile enrichment in folksonomy

Haoran XIE, Qing LI, Xudong MAO, Xiaodong LI, Yi CAI*, Yanghui RAO

*Corresponding author for this work

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

75 Citations (Scopus)

Abstract

In the era of big data, collaborative tagging (a.k.a. folksonomy) systems have proliferated as a consequence of the growth of Web 2.0 communities. Constructing user profiles from folksonomy systems is useful for many applications such as personalized search and recommender systems. The identification of latent user communities is one way to better understand and meet user needs. The behavior of users is highly influenced by the behavior of their neighbors or community members, and this can be utilized in constructing user profiles. However, conventional user profiling techniques often encounter data sparsity problems as data from a single user is insufficient to build a powerful profile. Hence, in this paper we propose a method of enriching user profiles based on latent user communities in folksonomy data. Specifically, the proposed approach contains four sub-processes: (i) tag-based user profiles are extracted from a folksonomy tripartite graph; (ii) a multi-faceted folksonomy graph is constructed by integrating tag and image affinity subgraphs with the folksonomy tripartite graph; (iii) random walk distance is used to unify various relationships and measure user similarities; (iv) a novel prototype-based clustering method based on user similarities is used to identify user communities, which are further used to enrich the extracted user profiles. To evaluate the proposed method, we conducted experiments using a public dataset, the results of which show that our approach outperforms previous ones in user profile enrichment.

Original languageEnglish
Pages (from-to)111-121
Number of pages11
JournalNeural Networks
Volume58
Early online date26 May 2014
DOIs
Publication statusPublished - Oct 2014
Externally publishedYes

Funding

The research presented in this paper has been supported by a Strategic Research Grant from the City University of Hong Kong (project no. 7002770 ), the National Natural Science Foundation of China (grant no. 61300137 ), the Guangdong Natural Science Foundation, China (no. S2013010013836 ), and the Fundamental Research Funds for the Central Universities, SCUT (no. 2014ZZ0035 ). We would like to give special thanks to James Lambert, a Ph.D. candidate in the Department of English, City University of Hong Kong, for proofreading the article.

Keywords

  • Big data
  • Community
  • Folksonomy
  • Social media
  • User profiling

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