Using Semi-Supervised Learning to Build Bayesian Network for Personal Preference Modeling in Home Environment

Smart home which understands user´s preference and provides right services at right time is the current trend. In this paper, we aim at developing a system which can achieve this objective by using the Bayesian network to model user´s preference. Instead of assuming the structure of Bayesian network is invariant, our system interacts with user appropriately to obtain some useful information and we use the semi-supervised learning with these information to both learn and adjust the Bayesian network for modeling the user´s preference in a more accurate manner. We can use preference model to provide adequate service in home environment. A simulation and a real home environment are constructed based on the proposed method, and the experiments also show the usefulness.