Topic-based web page recommendation using tags

Peng Jing, Daniel Zeng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

Collaborative tagging sites allow users to save and annotate their favorite web contents with tags. These tags provide a novel source of information for collaborative filtering. This paper proposes a probabilistic approach to leverage information embedded in tags to improve the effectiveness of Web page recommendation in a social information management context. In our approach, the probability of a Web page visit by a user is estimated by summing up the relevance of this Web page to this user's tags, and then those pages with the highest probabilities are recommended. Experiments using two real-world collaborative tagging datasets show that our algorithms outperform the common collaborative filtering methods.

Original languageEnglish (US)
Title of host publication2009 IEEE International Conference on Intelligence and Security Informatics, ISI 2009
Pages269-271
Number of pages3
DOIs
StatePublished - Oct 22 2009
Event2009 IEEE International Conference on Intelligence and Security Informatics, ISI 2009 - Dallas, TX, United States
Duration: Jun 8 2009Jun 11 2009

Publication series

Name2009 IEEE International Conference on Intelligence and Security Informatics, ISI 2009

Other

Other2009 IEEE International Conference on Intelligence and Security Informatics, ISI 2009
CountryUnited States
CityDallas, TX
Period6/8/096/11/09

Keywords

  • Collaborative filtering
  • Collaborative tagging
  • Probabilistic models
  • Social tag

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems
  • Software

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