A framework for tag-aware recommender systems

Hyunwoo Kim, Hyoung Joo Kim

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)


In social tagging system, a user annotates a tag to an item. The tagging information is utilized in recommendation process. In this paper, we propose a hybrid item recommendation method to mitigate limitations of existing approaches and propose a recommendation framework for social tagging systems. The proposed framework consists of tag and item recommendations. Tag recommendation helps users annotate tags and enriches the dataset of a social tagging system. Item recommendation utilizes tags to recommend relevant items to users. We investigate association rule, bigram, tag expansion, and implicit trust relationship for providing tag and item recommendations on the framework. The experimental results show that the proposed hybrid item recommendation method generates more appropriate items than existing research studies on a real-world social tagging dataset.

Original languageEnglish
Pages (from-to)4000-4009
Number of pages10
JournalExpert Systems With Applications
Issue number8
Publication statusPublished - 2014 Jun 15
Externally publishedYes


  • Hybrid framework
  • Recommendation
  • Social tagging system
  • Tags

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence


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