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POI 에서 딥러닝을 이용한 개인정보 보호 추천 시스템

Personal Information Protection Recommendation System using Deep Learning in POI

  • 펭소니 (순천향대학교 소프트웨어융합학과) ;
  • 박두순 (순천향대학교 소프트웨어융합학과) ;
  • 김대영 (순천향대학교 소프트웨어융합학과) ;
  • 양예선 (순천향대학교 소프트웨어융합학과) ;
  • 이혜정 (순천향대학교 AI.SW 교육원) ;
  • 싯소포호트 (순천향대학교 소프트웨어융합학과)
  • Peng, Sony (Dept. of Software Convergence, Soonchunhyang University) ;
  • Park, Doo-Soon (Dept. of Software Convergence, Soonchunhyang University) ;
  • Kim, Daeyoung (Dept. of Software Convergence, Soonchunhyang University) ;
  • Yang, Yixuan (Dept. of Software Convergence, Soonchunhyang University) ;
  • Lee, HyeJung (Institute for Artificial Intelligence and Software, Soonchunhyang University) ;
  • Siet, Sophort (Dept. of Software Convergence, Soonchunhyang University)
  • 발행 : 2022.11.21

초록

POI refers to the point of Interest in Location-Based Social Networks (LBSNs). With the rapid development of mobile devices, GPS, and the Web (web2.0 and 3.0), LBSNs have attracted many users to share their information, physical location (real-time location), and interesting places. The tremendous demand of the user in LBSNs leads the recommendation systems (RSs) to become more widespread attention. Recommendation systems assist users in discovering interesting local attractions or facilities and help social network service (SNS) providers based on user locations. Therefore, it plays a vital role in LBSNs, namely POI recommendation system. In the machine learning model, most of the training data are stored in the centralized data storage, so information that belongs to the user will store in the centralized storage, and users may face privacy issues. Moreover, sharing the information may have safety concerns because of uploading or sharing their real-time location with others through social network media. According to the privacy concern issue, the paper proposes a recommendation model to prevent user privacy and eliminate traditional RS problems such as cold-start and data sparsity.

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