A preference based recommendation system design through eye-tracking and social behavior analysis

Abstract

The study of recommendation services based on eye-tracking and social behavior analysis was conducted using either implicit or explicit data, and thus, carried the disadvantage of a decreased recommendation accuracy, having failed to supplement the flaws of each type of data. Therefore, the present study proposes a system applicable to recommendation services after deducting the personal preferences of the user by combining and analyzing the implicit data of eye-tracking and personal social behavior data with the explicit data of purchase data. By conducting experiments capable of obtaining category preferences based on smart phones, tablet PC, and smart TV, the study confirms changing preferences following the characteristics of the smart device. Ultimately, the study attempts to increase the accuracy of recommendations by using both implicit and explicit data and to achieve a recommendation system based on a collaborative filtering that considers device characteristics.