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

Qing DU, Haoran XIE, Yi CAI, Ho-fung LEUNG, Qing LI, Huaqing MIN, Fu Lee WANG

Research output: Contribution to journalArticle

26 Citations (Scopus)

Abstract

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. Copyright © 2016 Elsevier B.V.
Original languageEnglish
Pages (from-to)142-152
JournalNeurocomputing
Volume204
Early online dateApr 2016
DOIs
Publication statusPublished - Sep 2016

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Citation

Du, Q., Xie, H., Cai, Y., Leung, H.-f., Li, Q., Min, H., et al (2016). Folksonomy-based personalized search by hybrid user profiles in multiple levels. Neurocomputing, 204, 142-152.

Keywords

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