Research of computational customer modeling based on large-scale datasets and various situations in retail service

本研究利用基于品类的顾客行为知识发现，提出一个实际的服务支持系统。该方法是通过在百货连锁店中使用大量的销售点数据和ID-POS数据对具有某些购买情况或条件的客户购买行为进行建模来实现的。我们使用概率潜在语义索引，根据ID-POS数据中识别的购买模式自动生成客户和商品的类别。我们建立了一个贝叶斯网络模型，包括客户和商品类别、购买情况和条件、客户的属性和人口统计信息。该方法适用于各种业务领域（包括零售服务）的营销支持、服务建模和决策制定。

This research proposed an actual service support system using discovery of category-based customer behavior knowledge. The method is realized by modeling a customers' purchase behavior with some purchase situations or conditions using massive point of sales data with ID-POS data in a department store chain. We automatically generate categories of customers and items based on a purchase patterns identified in ID-POS data using probabilistic latent semantics indexing. We produce a Bayesian network model including the customer and item categories, situations and conditions of purchases, and the properties and demographic information of customers. This method is applicable for marketing support, service modeling, and decision making in various business fields, including retail services.