IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v60y2022i24p7551-7571.html
   My bibliography  Save this article

Matchmaking in reward-based crowdfunding platforms: a hybrid machine learning approach

Author

Listed:
  • Shaojian Qu
  • Lei Xu
  • Sachin Kumar Mangla
  • Felix T. S. Chan
  • Jianli Zhu
  • Sobhan Arisian

Abstract

Traditional clustering methods fail to accurately cluster the feature vectors of backers and macth the potential backers to compatible crowdfunding projects, mainly due to their sensitivity to the setting of the initial value. In this paper, we use the Apriori algorithm in conjunction with other machine learning tools to cluster the potential backers and provide more accurate recommendations for crowdfunding projects. Focusing on potential projects listed in a major reward-based crowdfunding platform, we first train the data obtained from the available list of backers. Using the Apriori algorithm, the degree of association between different project backers is then obtained, and weight calculation of the backers is carried out according to the association degree of the backers. The degree of association is used as a key index to cluster similar backers. Finally, we test the model and determine whether clustering can correctly classify the data in the test set based on the Apriori algorithm. Our experimental results show that there is 90% accuracy, precision and recall of the model. The proposed solution outperforms the other five benchmark methods and offers an imporved matchmaking by connecting the listed crowdfunding projects to the right backers.

Suggested Citation

  • Shaojian Qu & Lei Xu & Sachin Kumar Mangla & Felix T. S. Chan & Jianli Zhu & Sobhan Arisian, 2022. "Matchmaking in reward-based crowdfunding platforms: a hybrid machine learning approach," International Journal of Production Research, Taylor & Francis Journals, vol. 60(24), pages 7551-7571, December.
    • Handle: RePEc:taf:tprsxx:v:60:y:2022:i:24:p:7551-7571
    DOI: 10.1080/00207543.2022.2121870
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2022.2121870
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207543.2022.2121870?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.

    Citations

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


    Cited by:

    1. Hassan Abbas & Shu Tong, 2023. "Green Supply Chain Management Practices of Firms with Competitive Strategic Alliances—A Study of the Automobile Industry," Sustainability, MDPI, vol. 15(3), pages 1-21, January.
    2. Shengqing Chang & Jingjing Ding & Chenpeng Feng & Ruifeng Wang, 2024. "A Hybrid Parallel Processing Strategy for Large-Scale DEA Computation," Computational Economics, Springer;Society for Computational Economics, vol. 63(6), pages 2325-2349, June.
    3. Fang Xu & Mengfan Yan & Lun Wang & Shaojian Qu, 2022. "The Robust Emergency Medical Facilities Location-Allocation Models under Uncertain Environment: A Hybrid Approach," Sustainability, MDPI, vol. 15(1), pages 1-23, December.

    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:taf:tprsxx:v:60:y:2022:i:24:p:7551-7571. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/TPRS20 .

    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.