Exploring information hidden in tags:A subject-based item recommendation approach

Jing Peng, Daniel Zeng

Research output: Contribution to conferencePaper

5 Scopus citations

Abstract

Collaborative tagging sites allow users to bookmark and annotate their favorite Web contents with tags. These tags provide a novel source of information for collaborative filtering (CF). Research on how to improve item recommendation quality leveraging tags is emerging yet information hidden in tags is far from being fully exploited. In this paper, we aim at finding informative usage patterns from tags by consistent clustering on tags using nonnegative matrix factorization. The clustered subjects, represented by weighed tag vectors, can then be used to build a subjectcentered user information seeking model for item recommendation. Experiments on two realworld datasets show that our subject-based algorithms substantially outperform the traditional CF methods as well as tag-enhanced recommendation approaches reported in the literature.

Original languageEnglish (US)
Pages73-78
Number of pages6
StatePublished - Jan 1 2009
Event19th Workshop on Information Technologies and Systems, WITS 2009 - Phoenix, AZ, United States
Duration: Dec 14 2009Dec 15 2009

Other

Other19th Workshop on Information Technologies and Systems, WITS 2009
CountryUnited States
CityPhoenix, AZ
Period12/14/0912/15/09

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Keywords

  • Collaborative filtering
  • Collaborative tagging
  • Nonnegative matrix factorization
  • Tag-enhanced

ASJC Scopus subject areas

  • Information Systems
  • Control and Systems Engineering

Cite this

Peng, J., & Zeng, D. (2009). Exploring information hidden in tags:A subject-based item recommendation approach. 73-78. Paper presented at 19th Workshop on Information Technologies and Systems, WITS 2009, Phoenix, AZ, United States.