Google PageRank Algorithm: Markov Chain Model and Hidden Markov Model

التفاصيل البيبلوغرافية
العنوان: Google PageRank Algorithm: Markov Chain Model and Hidden Markov Model
المؤلفون: Prerna Rai, Arvind Lal
المصدر: International Journal of Computer Applications. 138:9-13
بيانات النشر: Foundation of Computer Science, 2016.
سنة النشر: 2016
مصطلحات موضوعية: Markov chain, Google matrix, Computer science, Computer Science::Information Retrieval, 05 social sciences, 050801 communication & media studies, computer.software_genre, Markov model, Viterbi algorithm, Computer Science::Digital Libraries, law.invention, symbols.namesake, 0508 media and communications, PageRank, law, 0502 economics and business, symbols, State space, Data mining, 050207 economics, Hidden Markov model, computer, Link analysis
الوصف: In this document, the algorithm behind Google PageRanking and their techniques have been put up. The basic algorithm used by Google, for PageRanking and other applications are Markov model or Markov Chain model and Hidden Markov model. 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 is a mathematical system model. It describes 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 a link analysis algorithm.
تدمد: 0975-8887
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_________::f22107cbf496cd0b05cfbc77bb34c725
https://doi.org/10.5120/ijca2016908942
حقوق: OPEN
رقم الأكسشن: edsair.doi...........f22107cbf496cd0b05cfbc77bb34c725
قاعدة البيانات: OpenAIRE