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Conference papers

Web Page Rank Prediction with Markov Models

Michalis Vazirgiannis 1 Dimitris Drosos 1 Pierre Senellart 2 Akrivi Vlachou 1
2 GEMO - Integration of data and knowledge distributed over the web
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : In this paper we propose a method for predicting the ranking position of a Web page. Assuming a set of successive past top-k rankings, we study the evolution of Web pages in terms of ranking trend sequences used for Markov Models training, which are in turn used to predict future rankings. The predictions are highly accurate for all experimental setups and similarity measures.
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Contributor : Pierre Senellart <>
Submitted on : Tuesday, March 4, 2008 - 11:06:28 AM
Last modification on : Thursday, July 8, 2021 - 3:48:41 AM
Long-term archiving on: : Friday, September 28, 2012 - 10:35:29 AM


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  • HAL Id : inria-00260431, version 1



Michalis Vazirgiannis, Dimitris Drosos, Pierre Senellart, Akrivi Vlachou. Web Page Rank Prediction with Markov Models. WWW, Apr 2008, Beijing, China. ⟨inria-00260431⟩



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