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

Cinicioglu E. N., Shenoy P. P.

ANNALS OF OPERATIONS RESEARCH, cilt.244, sa.2, ss.385-405, 2016 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 244 Sayı: 2
  • Basım Tarihi: 2016
  • Doi Numarası: 10.1007/s10479-012-1171-9
  • Dergi Adı: ANNALS OF OPERATIONS RESEARCH
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.385-405
  • Anahtar Kelimeler: Bayesian networks, Heuristic for Bayesian networks, RFID, Real-time recommendation systems, Targeted advertising
  • İstanbul Üniversitesi Adresli: Evet

Özet

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.