Learning aspects of interest from Gaze
Kei Shimonishi, Hiroaki Kawashima, Ryo Yonetani, Erina Ishikawa, Takashi Matsuyama · 2013
This paper presents a probabilistic framework to model the gaze generative process when a user is browsing a content consisting of multiple regions. The model enables us to learn multiple aspects of interest from gaze data, to represent and estimate user's interest as a mixture of aspects, and to predict gaze behavior in a unified framework. We recorded gaze data of subjects when they were browsing a digital pictorial book, and confirmed the effectiveness of the proposed model in terms of predicting the gaze target.