With the popularization of wireless Internet technology and smartphones, the importance of recommendation systems, which analyze personality of a user using social network data such as search history, contents of written articles, the number of accesses, and etc., to achieve user convenience to obtain high profit is increasing. Since existing recommendation systems usually use only single kind of data such as social network service (SNS) data or purchase histories, the analyzed user personality by the recommendation systems can be inaccurate. Hence, in this paper, we propose an intuitive and highly accurate recommendation system by collecting personal data of a user from SNS and eye-tracking data of the user. By analyzing eye-tracking and social behaviors, we formulate preference metrics to derive category preferences. Using the preference metrics, we yield user preferences for categories. In addition, by combining and analyzing common categories between the eye-tracking and the social behaviors, we yield a final preference. Also, using the Pearson correlation coefficients, we yield the similarity between users based on the category preferences. Our experimental results show that our recommendation accuracy is 98.5% for smart TV in average and 96.5% for smartphone in average. Also, we prove that the preferences of a user can vary according to smart devices by deriving the unconscious user preferences. To derive the unconscious user preferences, we collect eye-tracking data using multiple smart devices. Consequently, the results show the applicability of our proposed scheme in a recommendation system which considers characteristics of smart devices.
