Fuzzy web surfer models: theory and experiments

  • Authors:
  • Narayan L. Bhamidipati;Sankar K. Pal

  • Affiliations:
  • Machine Intelligence Unit, Indian Statistical Institute, Calcutta, India;Machine Intelligence Unit, Indian Statistical Institute, Calcutta, India

  • Venue:
  • WImBI'06 Proceedings of the 1st WICI international conference on Web intelligence meets brain informatics
  • Year:
  • 2006

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Abstract

A novel web surfer model, where the transition probabilities are fuzzy quantities, is proposed in this article. Based on the theory of Fuzzy Markov Chains, we introduce FuzzRank, which is the counterpart of PageRank. Apart from discussing the theoretical aspects of fuzzy surfer models and FuzzRank, we have also compared its ranking, convergence and robustness properties with PageRank. Extensive experimental results and a detailed example depict the advantages of FuzzRank over PageRank.