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The Application of Bayesian Learning in the Search Engine

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Abstract

Bayesian theory provides a method of calculating the assumed probability, based on the past records of the user who visit the Website we can calculate the probability of the information which the user will visit next time. According to the probability we can stored the data which the user may visit next time before he visits the Website to improve the performance of the search engine. In this paper according to the Bayesian theory we combined the Bayesian learning method with the search technology and then applicate it in the search engine to improve the query speed and reduced the redundant information of the search engine to improve its comprehensive performance.

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Authors and Affiliations

  1. School of Science, Jilin Institute of Chemical Technology, Jilin, China

    Ying Zhao

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  1. Ying Zhao
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Correspondence to Ying Zhao.

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Zhao, Y. The Application of Bayesian Learning in the Search Engine. Wireless Pers Commun 103, 1121–1131 (2018). https://doi.org/10.1007/s11277-018-5256-x

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  • DOI: https://doi.org/10.1007/s11277-018-5256-x

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