IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v244y2016i2d10.1007_s10479-012-1171-9.html
   My bibliography  Save this article

A new heuristic for learning Bayesian networks from limited datasets: a real-time recommendation system application with RFID systems in grocery stores

Author

Listed:
  • Esma Nur Cinicioglu

    (Istanbul University School of Business)

  • Prakash P. Shenoy

    (University of Kansas School of Business)

Abstract

Bayesian networks (BNs) are a useful tool for applications where dynamic decision-making is involved. However, it is not easy to learn the structure and conditional probability tables of BNs from small datasets. There are many algorithms and heuristics for learning BNs from sparse datasets, but most of these are not concerned with the quality of the learned network in the context of a specific application. In this research, we develop a new heuristic on how to build BNs from sparse datasets in the context of its performance in a real-time recommendation system. This new heuristic is demonstrated using a market basket dataset and a real-time recommendation model where all items in the grocery store are RFID tagged and the carts are equipped with an RFID scanner. With this recommendation model, retailers are able to do real-time recommendations to customers based on the products placed in cart during a shopping event.

Suggested Citation

  • Esma Nur Cinicioglu & Prakash P. Shenoy, 2016. "A new heuristic for learning Bayesian networks from limited datasets: a real-time recommendation system application with RFID systems in grocery stores," Annals of Operations Research, Springer, vol. 244(2), pages 385-405, September.
    • Handle: RePEc:spr:annopr:v:244:y:2016:i:2:d:10.1007_s10479-012-1171-9
    DOI: 10.1007/s10479-012-1171-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-012-1171-9
    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/s10479-012-1171-9?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. Daniel Zeng & Yong Liu & Ping Yan & Yanwu Yang, 2021. "Location-Aware Real-Time Recommender Systems for Brick-and-Mortar Retailers," INFORMS Journal on Computing, INFORMS, vol. 33(4), pages 1608-1623, October.
    2. K. Coussement & K. W. Bock & S. Geuens, 2022. "A decision-analytic framework for interpretable recommendation systems with multiple input data sources: a case study for a European e-tailer," Annals of Operations Research, Springer, vol. 315(2), pages 671-694, August.

    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:annopr:v:244:y:2016:i:2:d:10.1007_s10479-012-1171-9. 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.