关键词: 客户行为模型, 客户背景粒度, 朴素贝叶斯分类, 受试者运行特征曲线下面积, 统计显著性

Abstract: To investigate the context's influence on business customer consumption behavior, a sort of construction approach of context-based customer behavior model was proposed. One undergraduate customer transaction data online with three-level context granularity was collected and grouped on statistic based transaction data item. The classifiers including Naive Bayesian (NB), Tree Augmented NB (TAN) and Grouping and Counting-relational Database (GAC-RDB) were used to learn context and non-context predicating functions of each customer group. Based on the Area under a Receiver Operating Characteristic Curve (AUC) of predicating variable, the paper compared and analyzed quantitatively the effect of customer context when predicating his buying behavior. The experimental results demonstrate that the context information has preferable predication performance on the consumption decision of the customers especially the personalized customers.

Key words: customer behavior model, customer context granularity, Na?ve Bayes classification, Area Under a Receiver Operating Characteristic Curve (AUC), statistic significance