title: Exploring personalized searches using tag-based user profiles and resource profiles in folksonomy.
abstract: With the increase in resource-sharing websites such as YouTube and Flickr, many shared resources have arisen on the Web. Personalized searches have become more important and challenging since users demand higher retrieval quality. To achieve this goal, personalized searches need to take users' personalized profiles and information needs into consideration. Collaborative tagging (also known as folksonomy) systems allow users to annotate resources with their own tags, which provides a simple but powerful way for organizing, retrieving and sharing different types of social resources. In this article, we examine the limitations of previous tag-based personalized searches. To handle these limitations, we propose a new method to model user profiles and resource profiles in collaborative tagging systems. We use a normalized term frequency to indicate the preference degree of a user on a tag. A novel search method using such profiles of users and resources is proposed to facilitate the desired personalization in resource searches. In our framework, instead of the keyword matching or similarity measurement used in previous works, the relevance measurement between a resource and a user query (termed the query relevance) is treated as a fuzzy satisfaction problem of a user's query requirements. We implement a prototype system called the Folksonomy-based Multimedia Retrieval System (FMRS). Experiments using the FMRS data set and the MovieLens data set show that our proposed method outperforms baseline methods.
authors.affiliation: School of Software Engineering, South China University of Technology, Guangzhou, 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, 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, Kowloon, Hong Kong Special Administrative Region.
authors.forename: Haoran
authors.initials: H
authors.lastname: Xie
authors.affiliation: School of Software Engineering, South China University of Technology, Guangzhou, China.
authors.forename: Huaqin
authors.initials: H
authors.lastname: Min
mesh_terms: D006801:Humans; D007256:Information Systems / Q000639:trends; D060756:Social Networking; D014584:User-Computer Interface
keywords: Big data; Folksonomy; Personalized search; Social media; User profile