Abstract
This study tried to combine user interior location positioning data using Wi-Fi RSS technology with remote type eye tracking data, analyze it, and propose user modeling depending on individual user inclination. It grasped user inclination depending on the distance between a user and smart media and the difference in the screen size by conducting an experiment on smartphone, tablet PC, and notebook using a single camera. In addition, it classified public places such as a cafe or a library into open space and individual places such as a personal learning room into closed space, and then derived their correlations with concentration considering the type of space and the number of persons existing in the space. Through a combination between location positioning data and eye tracking data, it could reflect user inclination well and prove that users would show different user behaviors depending on changes in media and place.
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