Web page rank prediction with markov models

Authors:
Michalis Vazirgiannis;Dimitris Drosos;Pierre Senellart;Akrivi Vlachou
Affiliations:
INRIA Futurs, Orsay, France;Athens University of Economics and Business, Athens, Greece;INRIA Futurs & Universitéé Paris-Sud, Orsay, France;Athens University of Economics and Business, Athens, Greece
Venue:
Proceedings of the 17th international conference on World Wide Web
Year:
2008

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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.