Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Machine Learning for User Modeling
User Modeling and User-Adapted Interaction
Predictive Statistical Models for User Modeling
User Modeling and User-Adapted Interaction
User profiling in personal information agents: a survey
The Knowledge Engineering Review
Learning Bayesian Networks
Ensemble Pruning Via Semi-definite Programming
The Journal of Machine Learning Research
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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Collaborative approach is of crucial importance in user modeling to improve the individual prediction performance when only insufficient amount of data are available for each user. Existing methods such as collaborative filtering or multitask learning, however, have a limitation that they cannot readily incorporate a situation where individual tasks are required to model a complex dependency structure among the task-related variables, such as one by Bayesian networks. Motivated by this issue, we propose a general approach for collaboration which can be applied to Bayesian networks, based on a simple use of Bayesian principle. We demonstrate that the proposed method can improve both the prediction accuracy and its variance in many cases with insufficient data, in an experiment with a real-world dataset related to user modeling.