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  • 标题:Customer Behavior Prediction System by Large Scale Data Fusion in a Retail Service
  • 本地全文:下载
  • 作者:Tsukasa Ishigaki ; Takeshi Takenaka ; Yoichi Motomura
  • 期刊名称:人工知能学会論文誌
  • 印刷版ISSN:1346-0714
  • 电子版ISSN:1346-8030
  • 出版年度:2011
  • 卷号:26
  • 期号:6
  • 页码:670-681
  • DOI:10.1527/tjsai.26.670
  • 出版社:The Japanese Society for Artificial Intelligence
  • 摘要:This paper describes a computational customer behavior modeling by Bayesian network with an appropriate category. Categories are generated by a heterogeneous data fusion using an ID-POS data and customer's questionnaire responses with respect to their lifestyle. We propose a latent class model that is an extension of PLSI model. In the proposed model, customers and items are classified probabilistically into some latent lifestyle categories and latent item category. We show that the performance of the proposed model is superior to that of the k -means and PLSI in terms of category mining. We produce a Bayesian network model including the customer and item categories, situations and conditions of purchases. Based on that network structure, we can systematically identify useful knowledge for use in sustainable services. In the retail service, knowledge management with point of sales data mining is integral to maintaining and improving productivity. This method provides useful knowledge based on the ID-POS data for efficient customer relationship management and can be applicable for other service industries. This method is applicable for marketing support, service modeling, and decision making in various business fields, including retail services.
  • 关键词:service engineering ; large scale data modeling ; latent class model ; ID-POS data ; Bayesian network
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