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

74 Citations (Scopus)


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
Early online date26 May 2014
Publication statusPublished - Oct 2014
Externally publishedYes


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


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