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Fuzzy Web Surfer Models: Theory and Experiments

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Web Intelligence Meets Brain Informatics (WImBI 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4845))

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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.

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Authors and Affiliations

  1. Machine Intelligence Unit, Indian Statistical Institute, 203, B. T. Road, Calcutta - 700108, India

    Narayan L. Bhamidipati & Sankar K. Pal

Authors
  1. Narayan L. Bhamidipati
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  2. Sankar K. Pal
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Editor information

Ning Zhong Jiming Liu Yiyu Yao Jinglong Wu Shengfu Lu Kuncheng Li

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Bhamidipati, N.L., Pal, S.K. (2007). Fuzzy Web Surfer Models: Theory and Experiments. In: Zhong, N., Liu, J., Yao, Y., Wu, J., Lu, S., Li, K. (eds) Web Intelligence Meets Brain Informatics. WImBI 2006. Lecture Notes in Computer Science(), vol 4845. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77028-2_19

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  • DOI: https://doi.org/10.1007/978-3-540-77028-2_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-77027-5

  • Online ISBN: 978-3-540-77028-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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