Exploring social annotations with the application to web page recommendation

Hui Qian Li, Fen Xia, Daniel Zeng, Fei Yue Wang, Wen Ji Mao

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

Collaborative social annotation systems allow users to record and share their original keywords or tag attachments to Web resources such as Web pages, photos, or videos. These annotations are a method for organizing and labeling information. They have the potential to help users navigate the Web and locate the needed resources. However, since annotations are posted by users under no central control, there exist problems such as spam and synonymous annotations. To efficiently use annotation information to facilitate knowledge discovery from the Web, it is advantageous if we organize social annotations from semantic perspective and embed them into algorithms for knowledge discovery. This inspires the Web page recommendation with annotations, in which users and Web pages are clustered so that semantically similar items can be related. In this paper we propose four graphic models which cluster users, Web pages and annotations and recommend Web pages for given users by assigning items to the right cluster first. The algorithms are then compared to the classical collaborative filtering recommendation method on a real-world data set. Our result indicates that the graphic models provide better recommendation performance and are robust to fit for the real applications.

Original languageEnglish (US)
Pages (from-to)1028-1034
Number of pages7
JournalJournal of Computer Science and Technology
Volume24
Issue number6
DOIs
StatePublished - Nov 2009

Keywords

  • EM (expectation-maximization)
  • Graphic model
  • Recommendation
  • Social annotation
  • Tag

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computer Science Applications
  • Computational Theory and Mathematics

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