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

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.

[1]  Szymon Jaroszewicz,et al.  Interestingness of frequent itemsets using Bayesian networks as background knowledge , 2004, KDD.

[2]  M. S. Krishnan,et al.  A Field Study of RFID Deployment and Return Expectations , 2007 .

[3]  Peter Hicks,et al.  RFID and the book trade , 1999 .

[4]  Marek J. Druzdzel,et al.  Learning Bayesian network parameters from small data sets: application of Noisy-OR gates , 2001, Int. J. Approx. Reason..

[5]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[6]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[7]  David Maxwell Chickering,et al.  Dependency Networks for Inference, Collaborative Filtering, and Data Visualization , 2000, J. Mach. Learn. Res..

[8]  David Heckerman,et al.  Challenge: What is the Impact of Bayesian Networks on Learning? , 1997, IJCAI.

[9]  Fabio Gagliardi Cozman,et al.  Credal networks , 2000, Artif. Intell..

[10]  Geert Wets,et al.  Using association rules for product assortment decisions: a case study , 1999, KDD '99.

[11]  Anders L. Madsen,et al.  The Hugin Tool for Learning Bayesian Networks , 2003, ECSQARU.

[12]  Thomas Reutterer,et al.  An improved collaborative filtering approach for predicting cross-category purchases based on binary market basket data , 2003 .

[13]  Sandra L. Berger.  Massachusetts , 1896, The Journal of comparative medicine and veterinary archives.

[14]  Man Leung Wong,et al.  Bayesian variable selection for binary response models and direct marketing forecasting , 2010, Expert Syst. Appl..

[15]  Anna Goldenberg,et al.  Tractable learning of large Bayes net structures from sparse data , 2004, ICML.

[16]  Nir Friedman,et al.  Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm , 1999, UAI.

[17]  Prakash P. Shenoy,et al.  Use of Radio Frequency Identification for Targeted Advertising: A Collaborative Filtering Approach Using Bayesian Networks , 2007, ECSQARU.

[18]  Hans-Peter Kriegel,et al.  Ieee Transactions on Knowledge and Data Engineering Probabilistic Memory-based Collaborative Filtering , 2022 .

[19]  Thomas Dyhre Nielsen,et al.  Symbolic and Quantitative Approaches to Reasoning with Uncertainty , 2003, Lecture Notes in Computer Science.

[20]  B. Joseph Pine,et al.  The Experience Economy , 2020, Journal of Orthopaedic Experience & Innovation.

[21]  Feng Liu,et al.  An Improved Greedy Bayesian Network Learning Algorithm on Limited Data , 2007, ICANN.

[22]  Li Zhu,et al.  Data Mining on Imbalanced Data Sets , 2008, 2008 International Conference on Advanced Computer Theory and Engineering.

[23]  Paul Resnick,et al.  Recommender systems , 1997, CACM.

[24]  Marc H. J. Romanycia,et al.  What is a heuristic? , 1985 .

[25]  Klaus Finkenzeller,et al.  RFID Handbook: Radio-Frequency Identification Fundamentals and Applications , 2000 .

[26]  Weimin Xiao,et al.  Rule interestingness analysis using OLAP operations , 2006, KDD '06.

[27]  David Heckerman,et al.  Bayesian Networks for Data Mining , 2004, Data Mining and Knowledge Discovery.