Folksonomy-based personalized search by hybrid user profiles in multiple levels

Q. Du, H. Xie, Y. Cai, H.-F. Leung, Qing Li, H. Min, F.L. Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

31 Citations (Scopus)

Abstract

© 2016 Elsevier B.V.Recently, some systems have allowed users to rate and annotate resources, e.g., MovieLens, and we consider that it provides a way to identify favorite and non-favorite tags of a user by integrating his or her rating and tags. In this paper, we review and elaborate on the limitations of the current research on user profiling for personalized search in collaborative tagging systems. We then propose a new multi-level user profiling model by integrating tags and ratings to achieve personalized search, which can reflect not only a user's likes but also a his or her dislikes. To the best of our knowledge, this is the first effort to integrate ratings and tags to model multi-level user profiles for personalized search.
Original languageEnglish
Pages (from-to)142-152
Number of pages11
JournalNeurocomputing
Volume204
DOIs
Publication statusPublished - 5 Sep 2016
Externally publishedYes

Keywords

  • Folksonomy
  • Personalized search
  • Social tagging
  • User profiling
  • Web 2.0

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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