title: Community-aware user profile enrichment in folksonomy.
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.
authors.affiliation: Department of Computer Science, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong Special Administrative Region.
authors.forename: Haoran
authors.initials: H
authors.lastname: Xie
authors.affiliation: Department of Computer Science, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong Special Administrative Region; Multimedia Software Engineering Research Centre, City University of Hong Kong, Kowloon, Hong Kong Special Administrative Region.
authors.forename: Qing
authors.initials: Q
authors.lastname: Li
authors.affiliation: Department of Computer Science, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong Special Administrative Region.
authors.forename: Xudong
authors.initials: X
authors.lastname: Mao
authors.affiliation: Department of Computer Science, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong Special Administrative Region.
authors.forename: Xiaodong
authors.initials: X
authors.lastname: Li
authors.affiliation: School of Software Engineering, South China University of Technology, Guangzhou 510006, China. Electronic address: yicai.scut@gmail.com.
authors.forename: Yi
authors.initials: Y
authors.lastname: Cai
authors.affiliation: Department of Computer Science, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong Special Administrative Region.
authors.forename: Yanghui
authors.initials: Y
authors.lastname: Rao
mesh_terms: D016000:Cluster Analysis; D003299:Cooperative Behavior; D006801:Humans; D060756:Social Networking
keywords: Big data; Community; Folksonomy; Social media; User profiling