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首页> 外文期刊>Annals of Operations Research >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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A new heuristic for learning Bayesian networks from limited datasets: a real-time recommendation system application with RFID systems in grocery stores

机译：从有限数据集中学习贝叶斯网络的一种新启发式方法：杂货店中具有RFID系统的实时推荐系统应用程序

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摘要

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.
机译：贝叶斯网络（BN）是涉及动态决策的应用程序的有用工具。但是，从小型数据集中学习BN的结构和条件概率表并不容易。有很多算法和启发式算法可用于从稀疏数据集中学习BN，但其中大多数与特定应用环境下的学习网络质量无关。在这项研究中，我们针对如何在实时推荐系统中根据稀疏数据集的性能来开发BN进行了新的探索。使用市场购物篮数据集和实时推荐模型演示了这种新的启发式方法，其中，杂货店中的所有商品均已贴有RFID标签，并且推车配备了RFID扫描仪。通过这种推荐模型，零售商可以根据购物活动期间放入购物车中的产品向客户进行实时推荐。

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