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

Esma Nur Cinicioglu () and Prakash P. Shenoy ()
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Esma Nur Cinicioglu: Istanbul University School of Business
Prakash P. Shenoy: University of Kansas School of Business

Annals of Operations Research, 2016, vol. 244, issue 2, No 6, 385-405

Abstract: 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.

Keywords: Bayesian networks; Heuristic for Bayesian networks; RFID; Real-time recommendation systems; Targeted advertising (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10479-012-1171-9

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