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

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• 作者：Esma Nur Cinicioglu ; Prakash P. Shenoy
• 关键词：Bayesian networks ; Heuristic for Bayesian networks ; RFID ; Real ; time recommendation systems ; Targeted advertising
• 刊名：Annals of Operations Research
• 出版年：2016
• 出版时间：September 2016
• 年：2016
• 卷：244
• 期：2
• 页码：385-405
• 全文大小：1,309 KB
• 刊物类别：Business and Economics
• 刊物主题：Economics
  Operation Research and Decision Theory
  Combinatorics
  Theory of Computation
  
• 出版者：Springer Netherlands
• ISSN：1572-9338
• 卷排序：244

文摘

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.