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EC-Structure: Establishing Consumption Structure through Mining E-Commerce Data to Discover Consumption Upgrade

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  • Lin Guo
  • Dongliang Zhang

Abstract

The traditional methods of analyzing consumption structure have many limitations, and data acquisition is difficult, so it is hard to scientifically verify the accuracy of algorithms. With the development of Internet economy, many scientific researchers focus on mining knowledge of consumer behavior using big data analysis technology. Because consumption decisions are influenced by not only personal characteristics but also social trends and environment, it is one-sided to analyze the impact of one single factor on the phenomenon of consumption. The authors of this paper combine the consumption structure analysis method and data processing technology using data from an e-commerce platform to extract the consumption structure of cities, compare the structural differences between different periods, and then discover consumption upgrading according to swarm intelligence. The experiments prove the efficacy of the algorithm proposed in this paper compared to other similar algorithms using several different datasets, which illustrates the algorithm’s efficacy and stable performance in consumption structure analysis.

Suggested Citation

  • Lin Guo & Dongliang Zhang, 2019. "EC-Structure: Establishing Consumption Structure through Mining E-Commerce Data to Discover Consumption Upgrade," Complexity, Hindawi, vol. 2019, pages 1-8, March.
    • Handle: RePEc:hin:complx:6543590
    DOI: 10.1155/2019/6543590
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    References listed on IDEAS

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    1. Martin Zagler, 2017. "Empirical evidence on growth and business cycles," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 44(3), pages 547-566, August.
    2. Guo, Lin & Zuo, Wanli & Peng, Tao & Adhikari, Binod Kumar, 2015. "Attribute-based edge bundling for visualizing social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 48-55.
    3. Peter N. Ireland, 1995. "Using the permanent income hypothesis for forecasting," Economic Quarterly, Federal Reserve Bank of Richmond, issue Win, pages 49-63.
    4. Lance A. Fisher & Geoffrey Kingston, 2017. "Improved Forecasts of Tax Revenue via the Permanent Income Hypothesis," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 50(1), pages 21-31, March.
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    Cited by:

    1. Dong Guo & Lin Li & Lu Qiao & Fengyu Qi, 2023. "Digital economy and consumption upgrading: scale effect or structure effect?," Economic Change and Restructuring, Springer, vol. 56(6), pages 4713-4744, December.

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