Computational Customer Behavior Modeling for Knowledge Management with an Automatic Categorization Using Retail Services Datasets

摘要：

In the retail service, knowledge management with point of sales (POS) data mining is integral to maintaining and improving productivity. The present paper describes a method of computational customer behavior modeling based on real datasets, and we demonstrate some knowledge extractions from the model. The model is constructed by Bayesian network based on a large-scale POS dataset that incorporates customer identi.cation information and questionnaire responses. In addition, we employ an automatic categorization using probabilistic latent semantic indexing (PLSI), because an appropriate categorization of customers and items is needed for construction of a useful model in real services. We identify a number of categories with regard to customer behavior, and demonstrate the ef.cacy of our knowledge extraction approach for effective service provision and knowledge management.

关键词： service engineering large-scale data plobabilistic latent semantic indexing (PLSI) Bayesian network customer behavior modeling

作者: Tsukasa Ishigaki Takeshi Takenaka Yoichi Motomura

作者单位: Data Based Modeling Research Team, Center for Service Research National Institute of Advanced Industrial Science and Technology 2-3-26 Aomi, Koto-ku, Tokyo 135-0064, Japan

会议类型: 国际会议

会议名称: 2010 IEEE International Conference on e-Business Engineering(2010年电子商务工程国际研讨会 ICEBE 2010)

会议地点: 上海

会议语种:英文

页码: 528-533

在线出版日期: 2010-11-10（万方平台首次上网日期，不代表论文的发表时间）