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Construction of fast retrieval model of e-commerce supply chain information system based on Bayesian network

  • Le Kang
  • Yeping ChuEmail author
  • Kaijun Leng
  • Inneke Van Nieuwenhuyse
Original Article
  • 22 Downloads

Abstract

Bayesian network is a kind of uncertainty knowledge expression and reasoning tool, and it is an effective means to solve problems in related fields such as information retrieval. Considering the characteristics of e-commerce supply chain supply information and Bayesian network, a cognitive big data analysis method for intelligent information system is designed. The model uses a set of information sample documents to describe the query requirements and the documents to be detected. By calculating the similarity between them, the return results of the general search engine are sorted, thereby retrieving the supply chain supply information required by the user. Through numerical results, the precision of the source information retrieval model based on Bayesian network is also significantly higher than that of the trust network model and the inference network model, and the experimental data shows that the Bayesian network model has better retrieval performance than the trust network model and the inference network model. Therefore, when conducting large-scale e-commerce supply chain supply information collection, Bayesian network-based source information retrieval model is effective.

Keywords

Fast retrieval model E-commerce supply chain Bayesian network 

Notes

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Business AdministrationHubei University of EconomicsWuhanChina
  2. 2.National Academy of Economics StrategyChina Academy of Social SciencesBeijingChina
  3. 3.Faculty of Business EconomicsUniversiteit HasseltHasseltBelgium

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