Learning to estimate user interest utilizing the variational Bayes estimator

Many studies of man-machine interaction using eye trackers have been tackled over recent decades. In this paper, we present a new learning system to estimate user interest with gaze sensory information. In short, a statistical learning scheme, especially the variational Bayes (VB), is incorporated for building probabilistic model parameters, dealing with the uncertainty of estimated user interest. Several computational results show how the VB can cope with user interest estimation, by selectively modeling their uncertainty.