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Product Customer Demand Mining and Its Functional Attribute Configuration Driven by Big Data

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Data Science (ICPCSEE 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1257))

Abstract

The maturity of big data analysis theory and its tools improve the efficiency and reduce the cost of massive data mining. This paper discusses the method of product customer demand mining based on big data, and further studies the configuration of product function attributes. Firstly, the Hadoop platform was used to perform product attribute data participle and feature word extraction based on Apriori algorithm was used to mine product customer demand information. And then the MapReduce model on the big data platform was applied into efficient parallel data processing, obtaining product attributes with research value, and their weights and attribute levels. After that, the cloud model and the MNL model were employed to construct the product function attribute configuration model, and the improved artificial bee colony algorithm was used to solve the model. The optimal solution of the product function attribute configuration model was got. Finally, an example was given to illustrate the feasibility of the proposed method in this paper.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China granted 71961005 and the Guangxi Science and Technology Program granted 1598007-15.

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Authors and Affiliations

  1. College of Mechanical and Control Engineering, Guilin University of Technology, Guilin, 541004, Guangxi, China

    Dianting Liu & Kangzheng Huang

  2. College of Information Science and Engineering, Guilin University of Technology, Guilin, 541004, Guangxi, China

    Dianting Liu & Xia Huang

Authors
  1. Dianting Liu
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  2. Xia Huang
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  3. Kangzheng Huang
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Corresponding author

Correspondence to Dianting Liu .

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Editors and Affiliations

  1. North University of China, Taiyuan, China

    Jianchao Zeng

  2. Northeast Forestry University, Harbin, China

    Weipeng Jing

  3. Harbin University of Science and Technology, Harbin, China

    Xianhua Song

  4. National Academy of Guo Ding Institute of Data Science, Beijing, China

    Zeguang Lu

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Liu, D., Huang, X., Huang, K. (2020). Product Customer Demand Mining and Its Functional Attribute Configuration Driven by Big Data. In: Zeng, J., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2020. Communications in Computer and Information Science, vol 1257. Springer, Singapore. https://doi.org/10.1007/978-981-15-7981-3_11

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  • DOI: https://doi.org/10.1007/978-981-15-7981-3_11

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-7980-6

  • Online ISBN: 978-981-15-7981-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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