IDEAS home Printed from https://ideas.repec.org/a/hin/complx/6543590.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/8503/2019/6543590.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/8503/2019/6543590.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2019/6543590?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mary G. Finn, 1995. "Is \\"high\\" capacity utilization inflationary?," Economic Quarterly, Federal Reserve Bank of Richmond, issue Win, pages 1-16.
    2. Riccardo Corradini, 2005. "An Empirical Analysis of Permanent Income Hypothesis Applied to Italy using State Space Models with non zero correlation between trend and cycle," Computing in Economics and Finance 2005 28, Society for Computational Economics.
    3. Leonard I. Nakamura & Tom Stark, 2005. "Benchmark revisions and the U.S. personal saving rate," Working Papers 05-6, Federal Reserve Bank of Philadelphia.
    4. Kim, Kunhong & Hall, Viv B. & Buckle, Robert A., 2006. "Consumption-smoothing in a small, cyclically volatile open economy: Evidence from New Zealand," Journal of International Money and Finance, Elsevier, vol. 25(8), pages 1277-1295, December.
    5. Kevin Moran & Veronika Dolar, 2002. "Estimated DGE Models and Forecasting Accuracy: A Preliminary Investigation with Canadian Data," Staff Working Papers 02-18, Bank of Canada.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:complx:6543590. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.