Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Eye-tracking and social behavior preference-based recommendation system

Eye-tracking and social behavior preference-based recommendation system 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Journal of Supercomputing Springer Journals

Eye-tracking and social behavior preference-based recommendation system

Song, Hyejin; Moon, Nammee
The Journal of Supercomputing , Volume 75 (4) – May 30, 2018
Download PDF
17 pages

Loading next page...
 
/lp/springer_journal/eye-tracking-and-social-behavior-preference-based-recommendation-nGWoLlaXzY
Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Computer Science; Programming Languages, Compilers, Interpreters; Processor Architectures; Computer Science, general
ISSN
0920-8542
eISSN
1573-0484
DOI
10.1007/s11227-018-2447-x
Publisher site
See Article on Publisher Site

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 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.

Journal

The Journal of Supercomputing – Springer Journals

Published: May 30, 2018

References

  • Social big data: recent achievements and new challenges
    Bello-Orgaz, G; Jung, JJ; Camacho, D
  • Big data: a revolution that will transform how we live, work, and think
    John Walker, S
  • Malicious and spam posts in online social networks
    Abu-Nimeh, S; Chen, TM; Alzubi, O
  • Providing personalized recommendation for attending events based on individual interest profiles
    Galitsky, B
  • Efficiently processing skyline query on multi-instance data
    Chiu, S-I; Hsu, K-W
  • Personalized recommendation combining user interest and social circle
    Qian, X
  • Behind the scenes: a cross-country study into third-party website referencing and the online advertising ecosystem
    Nurse, JRC; Buckley, O
  • A real-time infrared LED detection method for input signal positioning of interactive media
    Song, W; Sun, G; Fong, S; Cho, K-E
  • Investigating users’ eye movement behavior in critiquing-based recommender systems
    Chen, L; Wang, F; Pu, P
  • Combining eye-tracking technologies with web usage mining for identifying Website Key objects
    Velasquez, JD
  • A method to utilize inner and outer SNS method for analyzing preferences
    Park, S-H; Kim, J
  • A feedbook page ranking and highlight contents selection scheme based on user interests
    Rew, J; Choi, Y-H; Hwang, E