IDEAS home Printed from https://ideas.repec.org/a/eee/jomega/v125y2024ics0305048323001834.html
   My bibliography  Save this article

Consumer preference analysis: Diverse preference learning with online ratings

Author

Listed:
  • Ren, Peijia
  • Liu, Xiaodan
  • Zhang, Wei-Guo

Abstract

In the information age, there is a growing need to process and analyze the great number of online reviews to understand consumer preferences and product reputations. Instead of addressing all online reviews as a simple group decision-making problem in the existing research, we propose a new preference learning (PL) mechanism to extract preferences by analyzing the diversity of preferences across different time frames. First, we collect and process online ratings from e-commerce platforms. Then, we construct an online optimization model based on online mirror descent to learn priority vectors that reflect various consumer preferences. We incorporate multiple learners to capture evolving preferences. In addition, we design experiments to verify the model involving the validity, robustness, as well as suggested parameters ranges. The model helps consumers and businesses capture ongoing preferences from massive online reviews. Importantly, the PL mechanism is innovatively designed to detect different preferences and learn different types of priorities with generating ratings online. The model offers more accurate preference information and represents a broader range of consumers’ behaviors.

Suggested Citation

  • Ren, Peijia & Liu, Xiaodan & Zhang, Wei-Guo, 2024. "Consumer preference analysis: Diverse preference learning with online ratings," Omega, Elsevier, vol. 125(C).
  • Handle: RePEc:eee:jomega:v:125:y:2024:i:c:s0305048323001834
    DOI: 10.1016/j.omega.2023.103019
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0305048323001834
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.omega.2023.103019?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.

    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:eee:jomega:v:125:y:2024:i:c:s0305048323001834. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/375/description#description .

    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.