Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
aHUGIN: a system creating adaptive causal probabilistic networks
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
Machine Learning - Special issue on learning with probabilistic representations
MailCat: an intelligent assistant for organizing e-mail
Proceedings of the third annual conference on Autonomous Agents
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Learning Bayesian nets that perform well
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Recognizing Time Pressure and Cognitive Load on the Basis of Speech: An Experimental Study
UM '01 Proceedings of the 8th International Conference on User Modeling 2001
Investigating the Possibility of Adaptation and Personalization in Virtual Environments
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
A Model of Temporally Changing User Behaviors in a Deployed Spoken Dialogue System
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
A personalized system for conversational recommendations
Journal of Artificial Intelligence Research
Predicting student help-request behavior in an intelligent tutor for reading
UM'03 Proceedings of the 9th international conference on User modeling
Adaptation in virtual environments: conceptual framework and user models
Multimedia Tools and Applications
Structured context prediction: a generic approach
DAIS'10 Proceedings of the 10th IFIP WG 6.1 international conference on Distributed Applications and Interoperable Systems
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Models of computer users that are learned on the basis of data can make use of two types of information: data about users in general and data about the current individual user. Focusing on user models that take the form of Bayesian networks, we compare four types of model that represent different ways of combining these two types of data. Models of the four types are applied to the data of an experiment, and they are evaluated according to theoretical, empirical, and practical criteria. One of the model types is a new variant of the AHUGIN method for adapting the probabilities of a Bayesian network while it is being used: Differential adaptation is a principled way of determining the speed with which each aspect of a network is adapted to an individual user.