IDEAS home Printed from https://ideas.repec.org/a/ids/ijnvor/v30y2024i1p82-99.html
   My bibliography  Save this article

Consumer preference mining method of online marketing platform based on social network analysis

Author

Listed:
  • Xiaodong Chen

Abstract

In order to address the problems of low text extraction accuracy, mining accuracy, and effectiveness in existing consumer preference mining methods for online marketing platforms, this article uses social network analysis methods to mine consumer preferences on online marketing platforms. First is the analysis of the node density and individual centrality in consumer social networks. Then, LDA model is selected to extract consumer texts from online marketing platforms, and rough set theory is combined for reduction processing. Finally, through the bidirectional association rule mining method, the consumer preferences of online marketing platforms are mined. The experimental results show that the text extraction accuracy of the proposed method is higher than 93%, and the minimum number of association rules can be reduced to below 2 × 103 N; the highest confidence level can reach 28.3%, and the highest support level can reach 97.5%, which can effectively explore consumer preferences on online marketing platforms.

Suggested Citation

  • Xiaodong Chen, 2024. "Consumer preference mining method of online marketing platform based on social network analysis," International Journal of Networking and Virtual Organisations, Inderscience Enterprises Ltd, vol. 30(1), pages 82-99.
    • Handle: RePEc:ids:ijnvor:v:30:y:2024:i:1:p:82-99
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=136776
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:ids:ijnvor:v:30:y:2024:i:1:p:82-99. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=22 .

    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.