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.
KeywordsBayesian networks Heuristic for Bayesian networks RFID Real-time recommendation systems Targeted advertising
This research was partly supported by Istanbul University research fund project number 6858. We are grateful for three anonymous reviewers of AnOR for comments and suggestions for improvements.
- Breese, J. S., Heckerman, D., & Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the fourteenth conference on uncertainty in artificial intelligence, Madison, WI. San Mateo: Morgan Kaufmann. Google Scholar
- Brijs, T., Swinnen, G., Vanhoof, K., & Wets, G. (1999). The use of association rules for product assortment decisions: a case study. In Proceedings of the fifth international conference on knowledge discovery and data mining, San Diego USA, August 15–18 (pp. 254–260). ISBN:1-58113-143-7. CrossRefGoogle Scholar
- Cavoukian, A. (2004). Tag, you’re it: privacy implications of radio frequency identification (RFID) technology. Toronto: Information and Privacy Commissioner. Google Scholar
- Cinicioglu, E. N., Shenoy, P. P., & Kocabasoglu, C. (2007). Use of radio frequency identification for targeted advertising: a collaborative filtering approach using Bayesian networks. In K. Mellouli (Ed.), Lecture notes in artificial intelligence: Vol. 4724. Symbolic and quantitative approaches to reasoning with uncertainty (pp. 889–900). Berlin: Springer. CrossRefGoogle Scholar
- Cui, G., Wong, M. L., & Zhang, G. (2010). In Bayesian variable selection for binary response models and direct marketing forecasting, expert systems with applications (Vol. 37, pp. 7656–7662). Google Scholar
- Finkenzeller, K. (1999). RFID handbook radio-frequency identification and applications. New York: John Wiley. Google Scholar
- Friedman, N., Goldszmidt, M., Heckerman, D., & Russell, S. (1997). Challange: what is the impact of Bayesian networks on learning? In Proceedings of the 15 th international joint conference on artificial intelligence (NIL-97) (pp. 10–15). Google Scholar
- Friedman, N., Nachman, L., & Pe’er, D. (1999). Learning Bayesian network structure from massive datasets: the “sparse candidate” algorithm. In Proc. fifteenth conference on uncertainty in artificial intelligence (UAI’ 99) (pp. 196–205). Google Scholar
- Goldenberg, A., & Moore, A. (2004). Tractable learning of large Bayes net structures from sparse data. In Proceedings of 21 st international conference on machine learning. Google Scholar
- Gu, Q., Cai, Z., Zhu, L., & Huang, B. (2008). Data mining on imbalanced data sets. In Proc. international conference on advanced computer theory and engineering (pp. 1020–1024). Google Scholar
- Heckerman, D., Chickering, D. M., Meek, C., Rounthwaite, R., & Kadie, C. (2000). Dependency networks for inference, collaborative filtering, and data visualization. Journal of Machine Learning Research, 1, 49–75. Google Scholar
- Liu, B., Zhao, K., Benkler, J., & Xiao, W. (2006). Rule interestingness analysis using OLAP operations. In Proc. ACM, KDD (pp. 297–306). Google Scholar
- Liu, F., Tian, F., & Zhu, Q. (2007). An improved greedy Bayesian network learning algorithm on limited data. In Marques de Sá et al. (Ed.), Lecture notes in computer science: Vol. 4668. ICANN 2007 (pp. 49–57). Berlin: Springer. Google Scholar
- Madsen, A., Lang, M., Kjaerulff, U., & Jensen, F. (2004). The Hugin tool for learning Bayesian networks. In Symbolic and quantitative approaches to reasoning with uncertainty (pp. 594–605). Berlin: Springer. Google Scholar
- Pine, B. J. II (1993). Mass customization. Boston: Harvard Business School Press. Google Scholar
- Pine, B. J. II, & Gilmore, J. H. (1999). The experience economy. Boston: Harvard Business School Press. Google Scholar
- Resnick, P., Iacovou, N., Suchak, M., Bergstorm, P., & Riedl, J. (1994). Grouplens: an open architecture for collaborative filtering of netnews. In Proceedings of the ACM conference on computer supported cooperative work (pp. 175–186). Google Scholar
- SC Digest Editorial Staff (2009, January). The five cent RFID tag is here. http://www.scdigest.com/assets/newsviews/09-01-27-2.pdf.
- Scuderi, M., & Clifton, K. (2005). Bayesian approaches to learning from data: using NHTS data for the analysis of land use and travel behavior. Bureau of Transportation Statistics, US Department of Transportation, Washington, DC. Google Scholar
- Yu, K., Schwaighofer A., Tresp, V., Xu, X., & Kriegel, H. P. (2004). Probabilistic memory-based collaborative filtering. IEEE Transactions on Knowledge and Data Engineering, 15(1), 56–69. Google Scholar