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Reseach Article

Commentary on Application of Hidden Markov Model in Google Page Ranking

Published on September 2016 by Prerna Rai, Moirangthem Goldie Meitei, Ferdousi Khatun, Abhijit Choudhury, Udit Kr. Chakraborty
International Conference on Computing and Communication
Foundation of Computer Science USA
ICCC2016 - Number 1
September 2016
Authors: Prerna Rai, Moirangthem Goldie Meitei, Ferdousi Khatun, Abhijit Choudhury, Udit Kr. Chakraborty
7c871757-fbeb-4726-873c-b9485da3d270

Prerna Rai, Moirangthem Goldie Meitei, Ferdousi Khatun, Abhijit Choudhury, Udit Kr. Chakraborty . Commentary on Application of Hidden Markov Model in Google Page Ranking. International Conference on Computing and Communication. ICCC2016, 1 (September 2016), 35-41.

@article{
author = { Prerna Rai, Moirangthem Goldie Meitei, Ferdousi Khatun, Abhijit Choudhury, Udit Kr. Chakraborty },
title = { Commentary on Application of Hidden Markov Model in Google Page Ranking },
journal = { International Conference on Computing and Communication },
issue_date = { September 2016 },
volume = { ICCC2016 },
number = { 1 },
month = { September },
year = { 2016 },
issn = 0975-8887,
pages = { 35-41 },
numpages = 7,
url = { /proceedings/iccc2016/number1/26157-cc58/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Computing and Communication
%A Prerna Rai
%A Moirangthem Goldie Meitei
%A Ferdousi Khatun
%A Abhijit Choudhury
%A Udit Kr. Chakraborty
%T Commentary on Application of Hidden Markov Model in Google Page Ranking
%J International Conference on Computing and Communication
%@ 0975-8887
%V ICCC2016
%N 1
%P 35-41
%D 2016
%I International Journal of Computer Applications
Abstract

In this document, the Google Page ranking and the algorithms behind this technique have been put up. Google, in its efforts to crawl and index the Web efficiently and produce much more satisfying search results than other existing systems, has been continuously working on optimizing its search results. This optimization is done by a mechanism called PageRank. Page ranking for Google has been implemented using Markov chain and we present here the use of Hidden Markov Model (HMM) in Google page ranking and its implications. These algorithms are used to search and rank websites in the Google search engine. PageRank is a way of measuring the importance of website pages. Markov chain model and Hidden Markov Model are mathematical system models. They describe transitions from one state to another in a state space. The Markov model is based on the probability the user will select the page and based on the number of incoming and outgoing links, ranks for the pages are determined. HMM also finds its application within Mapper/Reducer. These algorithms are link analysis algorithms. The current paper presents an analytical study on the application of HMM in Google Page Ranking.

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Index Terms

Computer Science
Information Sciences

Keywords

Damping Factor Links Markov Model Pagerank Probability Web-graph Stochastic