J Supercomput https://doi.org/10.1007/s11227-018-2447-x Eye-tracking and social behavior preference-based recommendation system 1 1 Hyejin Song · Nammee Moon © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract 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
The Journal of Supercomputing – Springer Journals
Published: May 30, 2018
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