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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
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    Cited by:

    1. 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.
    2. 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.

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