Machine Learning - Special issue on learning with probabilistic representations
Learning in graphical models
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Profiling your customers using Bayesian networks
ACM SIGKDD Explorations Newsletter
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
User modeling and adaptation in health promotion dialogs with an animated character
Journal of Biomedical Informatics - Special issue: Dialog systems for health communications
Service diffusion in the market considering consumers' subjective value
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Transdisciplinary Approach to Service Design Based on Consumer's Value and Decision Making
International Journal of Organizational and Collective Intelligence
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By understanding the behavior, satisfaction level, or values of the customer, the productivity and level of customer satisfaction of a service industry can be improved. Such customer-based considerations are estimated from questionnaire data in a general manner. The useful estimation of such considerations requires effective methods for modeling the cognitive structures of customers based on such data. However, it is difficult to model the behavior or decision making process of the customer, which involves nonlinear or non-Gaussian variables, using conventional statistical modeling techniques, which assume linear or Gaussian models. The present paper describes a method of constructing a probabilistic model of the cognitive structure of the customer, which clarifies the satisfaction level and decision making process of the customer of a retail service through statistical graphical modeling. The proposed method constructs a probabilistic cognitive structure model by integrating questionnaire data and a Bayesian network, which can handle nonlinear and non-Gaussian variables as conditional probabilities. The model structure can be constructed automatically based on information criteria and can embed some of the experiences of the model designer and/or physical or social rules in advance. The proposed method is applied to an analysis of the requested function from customers regarding the continued use of an item of interest. We obtained useful knowledge for function design and marketing from the model constructed by a simulation and sensitivity analysis. The proposed method can be applied to various services that use a variety of data.