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A Timeline-Based Algorithm for Personalized Tag Recommendation

  • Zhaohui Yu
  • Puwei Wang
  • Xiaoyong Du
  • Jianwei Cui
  • Tianren Xu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6724)

Abstract

Recently, tagging has been a flexible and important way to share and categorize web resources, these user-generated tags are effective to represent user interests because these tags reflect human being’s judgments while more concise and closer to human understanding, and the user interests are changing over time. Thus, modeling user interests to meet individual user needs is an important challenge for personalization and information filtering applications, such as recommender systems. In this paper, we apply a distance decay model for modeling user interests in terms of tags based on timeline. We then propose a novel algorithm to measure users’ similarities in terms of their tagging activity over a specific time period and provide personalized tag recommendation according to similar users’ interests in their next time intervals. Experimental results demonstrate the higher precision and recall with our personalized tag recommendation algorithm than other existing methods.

Keywords

timeline user interests distance decay model personalized tag recommendation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Zhaohui Yu
    • 1
    • 2
  • Puwei Wang
    • 1
    • 2
  • Xiaoyong Du
    • 1
    • 2
  • Jianwei Cui
    • 1
    • 2
  • Tianren Xu
    • 1
    • 2
  1. 1.Key Labs of Data Engineering and Knowledge Engineering Ministry of EducationChina
  2. 2.School of InformationRenmin University of ChinaBeijingChina

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