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

本研究提出了一个基于类别的客户行为知识发现的实际服务支持系统。该方法利用某连锁百货商店的海量销售点数据和ID-POS数据，在一定的购买情景或条件下，对顾客的购买行为进行建模。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，以及客户的属性和人口统计信息。这种方法适用于包括零售服务在内的各个商业领域的营销支持、服务建模和决策。

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.