Abstract
Markov chains refer to stochastic processes whose states change according to transition probabilities determined only by the states of the previous time step. They have been crucial for modeling large-scale systems with random behavior in various fields such as control, communications, biology, optimization, and economics. In this entry, we focus on their recent application to the area of search engines, namely, the PageRank algorithm employed at Google, which provides a measure of importance for each page in the web. We present several researches carried out with control theoretic tools such as aggregation, distributed randomized algorithms, and PageRank optimization. Due to the large size of the web, computational issues are the underlying motivation of these studies.
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Ishii, H., Tempo, R. (2013). Markov Chains and Ranking Problems in Web Search. In: Baillieul, J., Samad, T. (eds) Encyclopedia of Systems and Control. Springer, London. https://doi.org/10.1007/978-1-4471-5102-9_135-1
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DOI: https://doi.org/10.1007/978-1-4471-5102-9_135-1
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Online ISBN: 978-1-4471-5102-9
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Latest
Markov Chains and Ranking Problems in Web Search- Published:
- 29 November 2019
DOI: https://doi.org/10.1007/978-1-4471-5102-9_135-2
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Original
Markov Chains and Ranking Problems in Web Search- Published:
- 12 February 2014
DOI: https://doi.org/10.1007/978-1-4471-5102-9_135-1