IDEAS home Printed from https://ideas.repec.org/h/spr/advbcp/978-94-6463-636-9_4.html

Optimization and Theoretical Exploration of Intelligent Advertising System Based on Big Data and Artificial Intelligence

In: Proceedings of the 2024 International Conference on Digital Economy and Marxist Economics (ICDEME 2024)

Author

Listed:
  • Lan Shi

    (Guangxi University of Finance and Economics, School of Journalism and Cultural Communication)

  • Dandan Lu

    (Guangxi University of Finance and Economics, School of Business Administration)

Abstract

Intelligent advertising combines AI and big data technologies to achieve accurate placement of personalized advertising content by analyzing user behavior and preferences. This paper introduces the construction concept of AdMind, a smart advertising platform, which realizes the whole-process intelligent management of the advertising business process through a full-stack AI tool chain, from data set interface to advertising effect tracking. AdMind platform automates the generation of advertising creative, optimizes the delivery strategy, and monitors the advertising effect in real time through deep learning and intelligent algorithms, forming a self-perfecting closed-loop system. Meanwhile, this paper also looks forward to the future application prospects of smart advertising, including the potential impact of deep learning, edge computing and blockchain technology in improving advertising accuracy and user experience.

Suggested Citation

  • Lan Shi & Dandan Lu, 2024. "Optimization and Theoretical Exploration of Intelligent Advertising System Based on Big Data and Artificial Intelligence," Advances in Economics, Business and Management Research, in: Yongjun Guan & Yan Duan & Tao Wang & Chuan Liang (ed.), Proceedings of the 2024 International Conference on Digital Economy and Marxist Economics (ICDEME 2024), pages 20-28, Springer.
  • Handle: RePEc:spr:advbcp:978-94-6463-636-9_4
    DOI: 10.2991/978-94-6463-636-9_4
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:spr:advbcp:978-94-6463-636-9_4. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.