Title :
Preference learning for affective modeling
Author :
Yannakakis, Georgios N.
Author_Institution :
Center for Comput. Games Res., IT Univ. of Copenhagen, Copenhagen, Denmark
Abstract :
There is an increasing trend towards personalization of services and interaction. The use of computational models for learning to predict user emotional preferences is of significant importance towards system personalization. Preference learning is a machine learning research area that aids in the process of exploiting a set of specific features of an individual in an attempt to predict her preferences. This paper outlines the use of preference learning for modeling emotional preferences and shows the methodology´s promise for constructing accurate computational models of affect.
Keywords :
behavioural sciences computing; learning (artificial intelligence); affective modeling; computational models; interaction personalization; machine learning; preference learning; service personalization; user emotional preference prediction; Computational modeling; Control systems; Gaussian processes; Humans; Instruments; Interactive systems; Machine learning; Neural networks; Predictive models; Protocols;
Conference_Titel :
Affective Computing and Intelligent Interaction and Workshops, 2009. ACII 2009. 3rd International Conference on
Conference_Location :
Amsterdam
Print_ISBN :
978-1-4244-4800-5
Electronic_ISBN :
978-1-4244-4799-2
DOI :
10.1109/ACII.2009.5349491