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Computing Personalized PageRank Based on Temporal-Biased Proximity

  • Bundit Manaskasemsak
  • Pramote Teerasetmanakul
  • Kankamol Tongtip
  • Athasit Surarerks
  • Arnon Rungsawang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 253)

Abstract

Dynamic behaviors of World Wide Web is one of the most important characteristics that challenge search engine administrators to manipulate their search collection. Web content and links are changed each day to provide up-to-date information. In addition, a fresh web page, like new news article, is often more interesting to web users than a stale one. Thus, an analysis of temporal activities of the Web can contribute to improve better search and result ranking. In this paper, we propose a web personalized link-based ranking scheme that incorporates temporal information extracted from historical page activities. We first quantify page modifications over time and design a time-proximity model used in calculating inverse propagation scores of web pages. These scores are then used as a bias of personalized PageRank for page authority assessment. We conduct the experiments on a real-world web collection gathered from the Internet Archive. The results show that our approach improves upon PageRank in ranking of search results with respect to human users’ preference.

Keywords

Temporal-biased personalized PageRank Time-proximity model Temporal analysis PageRank computation Web ranking algorithm 

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Bundit Manaskasemsak
    • 1
  • Pramote Teerasetmanakul
    • 1
  • Kankamol Tongtip
    • 2
  • Athasit Surarerks
    • 2
  • Arnon Rungsawang
    • 1
  1. 1.Massive Information and Knowledge Engineering Laboratory, Department of Computer EngineeringKasetsart UniversityBangkokThailand
  2. 2.Engineering Laboratory in Theoretical Enumerable System,Department of Computer EngineeringChulalongkorn UniversityBangkokThailand

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