IDEAS home Printed from https://ideas.repec.org/a/spr/elcore/v20y2020i2d10.1007_s10660-020-09411-6.html
   My bibliography  Save this article

Product advertising recommendation in e-commerce based on deep learning and distributed expression

Author

Listed:
  • Lichun Zhou

    (Shangqiu Normal University)

Abstract

With the advent of Internet big data era, recommendation system has become a hot research topic of information selection. This paper studies the application of deep learning and distributed expression technology in e-commerce product advertising recommendation. In this paper, firstly, from the semantic level of advertising, we build a similarity network based on the theme distribution of advertising, and then build a deep learning model framework for advertising click through rate prediction. Finally, we propose an improved recommendation algorithm based on recurrent neural network and distributed expression. Aiming at the particularity of the recommendation algorithm, this paper improves the traditional recurrent neural network, and introduces a time window to control the hidden layer data transfer of the recurrent neural network. The experimental results show that the improved recurrent neural network model based on time window is superior to the traditional recurrent neural network model in the accuracy of recommendation system. The complexity of calculation is reduced and the accuracy of recommendation system is improved.

Suggested Citation

  • Lichun Zhou, 2020. "Product advertising recommendation in e-commerce based on deep learning and distributed expression," Electronic Commerce Research, Springer, vol. 20(2), pages 321-342, June.
    • Handle: RePEc:spr:elcore:v:20:y:2020:i:2:d:10.1007_s10660-020-09411-6
    DOI: 10.1007/s10660-020-09411-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10660-020-09411-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10660-020-09411-6?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
    ---><---

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

    References listed on IDEAS

    as
    1. Gary W. Williams & Oral Capps, Jr. & Daniel Hanselka, 2017. "U.S. National Economic Contribution of Generic Food and Agricultural Product Advertising," Reports 285264, Texas A&M University, Agribusiness, Food, and Consumer Economics Research Center.
    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. Saravanan Thirumuruganathan & Soon-gyo Jung & Dianne Ramirez Robillos & Joni Salminen & Bernard J. Jansen, 2021. "Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm?," Electronic Commerce Research, Springer, vol. 21(1), pages 73-100, March.
    2. Robert RUSU & Constantin AVRAM, 2022. "Deep Learning Systems Integrated into the Digital Strategy of a Company Involved in e-commerce," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 1, pages 5-10.
    3. Arodh Lal Karn & Rakshha Kumari Karna & Bhavana Raj Kondamudi & Girish Bagale & Denis A. Pustokhin & Irina V. Pustokhina & Sudhakar Sengan, 2023. "RETRACTED ARTICLE: Customer centric hybrid recommendation system for E-Commerce applications by integrating hybrid sentiment analysis," Electronic Commerce Research, Springer, vol. 23(1), pages 279-314, March.
    4. Jaewoong Choi & Jiho Lee & Janghyeok Yoon & Sion Jang & Jaeyoung Kim & Sungchul Choi, 2022. "A two-stage deep learning-based system for patent citation recommendation," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6615-6636, November.
    5. Maria Cristina ENACHE, 2023. "Python for e-Commerce," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 2, pages 122-127.
    6. Aitor Goti & Leire Querejeta-Lomas & Aitor Almeida & José Gaviria de la Puerta & Diego López-de-Ipiña, 2023. "Artificial Intelligence in Business-to-Customer Fashion Retail: A Literature Review," Mathematics, MDPI, vol. 11(13), pages 1-32, June.
    7. Bernd Heinrich & Marcus Hopf & Daniel Lohninger & Alexander Schiller & Michael Szubartowicz, 2022. "Something’s Missing? A Procedure for Extending Item Content Data Sets in the Context of Recommender Systems," Information Systems Frontiers, Springer, vol. 24(1), pages 267-286, February.
    8. Miao, Ruomu & Li, Benqian, 2022. "A user-portraits-based recommendation algorithm for traditional short video industry and security management of user privacy in social networks," Technological Forecasting and Social Change, Elsevier, vol. 185(C).
    9. Weiwei Deng, 2022. "Leveraging consumer behaviors for product recommendation: an approach based on heterogeneous network," Electronic Commerce Research, Springer, vol. 22(4), pages 1079-1105, December.
    10. Mozhu Wang & Jianming Yao, 2023. "A reliable location design of unmanned vending machines based on customer satisfaction," Electronic Commerce Research, Springer, vol. 23(1), pages 541-575, March.

    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.

      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:elcore:v:20:y:2020:i:2:d:10.1007_s10660-020-09411-6. 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: 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.