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Computer and Information Sciences III pp 409–417Cite as

Integrating Semantic Tagging with Popularity-Based Page Rank for Next Page Prediction

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Abstract

In this work, we present a next page prediction method that is based on semantic classification of Web pages supported with Popularity based Page Rank (PPR) technique. As the first step, we use a model that basically uses Web page URLs in order to classify Web pages semantically. By using this semantic information, next page is predicted according to the semantic similarity of Web pages. At this point, we augment the technique with Popularity based Page Rank (PPR) values of each Web page. PPR is a type of Page Rank algorithm that is biased with page visit duration, frequency of page visits and the size of the Web page. The accuracy of the proposed method is tested with a set of experiments in comparison to that of two similar approaches in the literature.

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Notes

  1. 1.

    The similarity value is calculated from common John tagging in P1 and P2.

  2. 2.

    http://www.ceng.metu.edu.tr/

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Author information

Authors and Affiliations

  1. METU Computer Engineering Department, 06800 , Ankara, Turkey

    Banu Deniz Gunel & Pinar Senkul

Authors
  1. Banu Deniz Gunel
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  2. Pinar Senkul
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Corresponding author

Correspondence to Pinar Senkul .

Editor information

Editors and Affiliations

  1. , Dept of Electrical and Electronics Eng'g, Imperial College, London, SW7 2BT, United Kingdom

    Erol Gelenbe

  2. , EEE Dept., Imperial College, Exhibition Rd., London, SW72AZ, United Kingdom

    Ricardo Lent

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© 2013 Springer-Verlag London

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Gunel, B.D., Senkul, P. (2013). Integrating Semantic Tagging with Popularity-Based Page Rank for Next Page Prediction. In: Gelenbe, E., Lent, R. (eds) Computer and Information Sciences III. Springer, London. https://doi.org/10.1007/978-1-4471-4594-3_42

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  • DOI: https://doi.org/10.1007/978-1-4471-4594-3_42

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-4593-6

  • Online ISBN: 978-1-4471-4594-3

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