Exploring personalized searches using tag-based user profiles and resource profiles in folksonomy

Y. Cai, Qing Li, H. Xie, H. Min

Research output: Journal article publicationJournal articleAcademic researchpeer-review

40 Citations (Scopus)


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. © 2014 Elsevier Ltd.
Original languageEnglish
Pages (from-to)98-110
Number of pages13
JournalNeural Networks
Publication statusPublished - 1 Jan 2014
Externally publishedYes


  • Big data
  • Folksonomy
  • Personalized search
  • Social media
  • User profile

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence


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