Machine Learning - Special issue on learning with probabilistic representations
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
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
A Context-Aware Movie Preference Model Using a Bayesian Network for Recommendation and Promotion
UM '07 Proceedings of the 11th international conference on User Modeling
A Bayesian multiresolution independence test for continuous variables
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
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In service industries, matching the level of demand of the consumer and the level of service of the provider is important because it requires the service provider to have knowledge of consumer-related factors. Therefore, an intelligent model of the consumer is needed to estimate such factors because they cannot be observed directly by the service provider. This paper describes a method for computational modeling of the consumer by understanding his or her behavior based on datasets observed in real services. The proposed method constructs a probabilistic structure model by integrating questionnaire data and a Bayesian network, which incorporates nonlinear and non-Gaussian variables as conditional probabilities. The proposed method is applied to an analysis of the requested function from customers regarding the continued use of an item of interest. The authors obtained useful knowledge for function design and marketing from the constructed model by a simulation and sensitivity analysis.