Cluster Based Personalized Search

  • Hyun Chul Lee
  • Allan Borodin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5427)


We study personalized web ranking algorithms based on the existence of document clusterings. Motivated by the topic sensitive page ranking of Haveliwala [20], we develop and implement an efficient “local-cluster” algorithm by extending the web search algorithm of Achlioptas et al. [10]. We propose some formal criteria for evaluating such personalized ranking algorithms and provide some preliminary experiments in support of our analysis. Both theoretically and experimentally, our algorithm differs significantly from Topc Sensitive Page Rank.


Latent Semantic Analysis Ranking Algorithm PageRank Algorithm Personalized Search Page Ranking 
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 2009

Authors and Affiliations

  • Hyun Chul Lee
    • 1
  • Allan Borodin
    • 2
  1. 1.Thoora.comTorontoCanada
  2. 2.DCSUniversity of TorontoTorontoCanada

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