Integrating Temporal Usage Pattern into Personalized Tag Prediction

  • Lei Zhang
  • Jian Tang
  • Ming Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7235)

Abstract

The emergence of social tagging systems enables users to organize and share their interested resources. In order to ease the human-computer interaction with such systems, extensive researches have been done on how to recommend personalized tags for rescources. These researches mainly consider user profile, resource content, or the graph structure of users, resources and tags. Users’ preferences towards different tags are usually regarded as invariable against time, neglecting the switch of users’ short-term interests. In this paper, we examine the temporal factor in users’ tagging behaviors by investigating the occurrence patterns of tags and then incorporate this into a novel method for ranking tags. To assess a tag for a user-resource pair, we first consider the user’s general interest in it, then we calculate its recurrence probability based on the temporal usage pattern, and at last we consider its tag relevance to the content of the post. Experiments conducted on real datasets from Bibsonomy and Delicious demonstrate that our method outperforms other temporal models and state-of-the-art tag prediction methods.

Keywords

Real Time Interval Social Bookmark Recurrence Probability Resource Content Recurrence Dynamic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Lei Zhang
    • 1
  • Jian Tang
    • 1
  • Ming Zhang
    • 1
  1. 1.School of Electronics Engineering and Computer SciencePeking UniversityBeijingChina

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