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.
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Notes
The Netflix prize competition seeks to substantially improve the accuracy of predictions about how much someone is going to love a movie based on the ratings of the movies they have already seen.
Although most of the products are identified by a unique barcode, some article numbers in the dataset represent a group of products rather than an individual product item (Brijs et al. 1999).
This number is the sum of each of these products’ count for the baskets they are in.
Rounded to two decimal places 8897/2562=3.47.
In this dataset 3.47 indicates the point where a big drop in the sales figures of the most purchased products happen.
Throughout the paper the “final BN” phrase will refer to the BN which is created on the last step of the application of the heuristic after the variables to be included in the BN are determined. This final BN constitutes the basis for the performance evaluation of the proposed heuristic.
The average probability of correct prediction for the marginal model is 78.2288 % in this case.
BNs created without the using the heuristic.
While some products are common in each of the most purchased 30 products lists (mostly the most purchased top five and their frequencies do differ), the variety of the products included in the data sets are different for all of the four data sets.
The CPTs are obtained from the BNs created with the 6th–30th most purchased products.
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Acknowledgement
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.
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Cinicioglu, E.N., Shenoy, P.P. A new heuristic for learning Bayesian networks from limited datasets: a real-time recommendation system application with RFID systems in grocery stores. Ann Oper Res 244, 385–405 (2016). https://doi.org/10.1007/s10479-012-1171-9
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DOI: https://doi.org/10.1007/s10479-012-1171-9