Web page rank prediction with markov models

Michalis Vazirgiannis, Dimitris Drosos, Pierre Senellart, Akrivi Vlachou

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.