@inproceedings{ce491166f67345b38e5dd860ba6a4c48,
title = "Web page rank prediction with markov models",
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.",
keywords = "Markov models, Ranking prediction",
author = "Michalis Vazirgiannis and Dimitris Drosos and Pierre Senellart and Akrivi Vlachou",
year = "2008",
month = dec,
day = "15",
doi = "10.1145/1367497.1367663",
language = "English",
isbn = "9781605580852",
series = "Proceeding of the 17th International Conference on World Wide Web 2008, WWW'08",
pages = "1075--1076",
booktitle = "Proceeding of the 17th International Conference on World Wide Web 2008, WWW'08",
note = "17th International Conference on World Wide Web 2008, WWW'08 ; Conference date: 21-04-2008 Through 25-04-2008",
}