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Using Markov Chains for Structural Link Prediction in Adaptive Web Sites

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  • First Online:
User Modeling 2001 (UM 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2109))

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Abstract

My research investigates into using Markov chains to make link prediction and the transition matrix derived from Markov chains to acquire structural knowledge about Web sites. The structural knowledge is acquired in the form of three types of clusters: hierarchical clusters, reference clusters, and grid clusters. The predicted Web pages and acquired Web structures are further integrated to assist Web users in their navigation in the Web site.

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References

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Author information

Authors and Affiliations

  1. School of Information and Software Engineering, University of Ulster at Jordanstown, Newtownabbey, Co. Antrim, BT37 0QB, UK

    Jianhan Zhu

Authors
  1. Jianhan Zhu
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Editor information

Editors and Affiliations

  1. German Research Center for Artificial Intelligence (DFKI GmbH), Stuhlsatzenhausweg 3, 66123, Saarbrücken, Germany

    Mathias Bauer

  2. University of Texas, 19015, Arlington, TX, 76019-0015, USA

    Piotr J. Gmytrasiewicz

  3. Department of Computer Science ARIES, University of Saskatchewan, 1C101 Engineering Bldg., 57 Campus Dr., Saskatoon, SK, Canada, S7N 5A9

    Julita Vassileva

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© 2001 Springer-Verlag Berlin Heidelberg

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Zhu, J. (2001). Using Markov Chains for Structural Link Prediction in Adaptive Web Sites. In: Bauer, M., Gmytrasiewicz, P.J., Vassileva, J. (eds) User Modeling 2001. UM 2001. Lecture Notes in Computer Science(), vol 2109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44566-8_51

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  • DOI: https://doi.org/10.1007/3-540-44566-8_51

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42325-6

  • Online ISBN: 978-3-540-44566-1

  • eBook Packages: Springer Book Archive

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