Hierarchical Structure Learning in a Bayesian Network for the Analysis of Purchasing Behavior
Ryota Kawabe, Hiroaki Ito, Haruka Yamashita, Masayuki Goto · Total Quality Science · 2019
In the original Bayesian network model, the hierarchical structure of the variables is not assumed.When modeling the relation between the sales of products in a retail industry, it is better to consider a hierarchical structure of items (e.g., first, second, and third classifications).To apply a Bayesian network to such data, we have to focus on one hierarchy only in order to acquire a Bayesian network model.However, focusing only on the first or second classification provides a high-level view, but makes it difficult to understand customers' purchasing behavior in detail.On the other hand, focusing on the third classification results in a considerable number of nodes and a complicated network structure.Thus, capturing the overall relationship between products is not straightforward.Therefore, we propose a hierarchical Bayesian network model and a new learning method based on max-min hill-climbing learning algorithm.In the proposed method, we focus on the hierarchical structure of products, which enables us to construct a lower layer that considers the causal relationships in the upper layer.Furthermore, we investigate a case study using actual hierarchical data on consumer purchases, and demonstrate the proposed model using a simulation analysis.