title: VisitSense: Sensing Place Visit Patterns from Ambient Radio on Smartphones for Targeted Mobile Ads in Shopping Malls.
abstract: In this paper, we introduce a novel smartphone framework called VisitSense that automatically detects and predicts a smartphone user's place visits from ambient radio to enable behavioral targeting for mobile ads in large shopping malls. VisitSense enables mobile app developers to adopt visit-pattern-aware mobile advertising for shopping mall visitors in their apps. It also benefits mobile users by allowing them to receive highly relevant mobile ads that are aware of their place visit patterns in shopping malls. To achieve the goal, VisitSense employs accurate visit detection and prediction methods. For accurate visit detection, we develop a change-based detection method to take into consideration the stability change of ambient radio and the mobility change of users. It performs well in large shopping malls where ambient radio is quite noisy and causes existing algorithms to easily fail. In addition, we proposed a causality-based visit prediction model to capture the causality in the sequential visit patterns for effective prediction. We have developed a VisitSense prototype system, and a visit-pattern-aware mobile advertising application that is based on it. Furthermore, we deploy the system in the COEX Mall, one of the largest shopping malls in Korea, and conduct diverse experiments to show the effectiveness of VisitSense.
authors.affiliation: School of Computing, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong, Daejeon 305-338, Korea. bjkim@nclab.kaist.ac.kr.
authors.forename: Byoungjip
authors.initials: B
authors.lastname: Kim
authors.affiliation: School of Computer Science and Engineering, Korea University of Technology and Education (KOREATECH), 1600 Chungjeol-ro, Cheonan, Chungcheong 330-708, Korea. swkang@koreatech.ac.kr.
authors.forename: Seungwoo
authors.initials: S
authors.lastname: Kang
authors.affiliation: Department of Computer and Communications Engineering, Kangwon National University (KNU), 1 Kangwondaehak-gil, Chuncheon, Gangwon 200-701, Korea. jyha@kangwon.ac.kr.
authors.forename: Jin-Young
authors.initials: JY
authors.lastname: Ha
authors.affiliation: School of Computing, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong, Daejeon 305-338, Korea. junesong@nclab.kaist.ac.kr.
authors.forename: Junehwa
authors.initials: J
authors.lastname: Song
keywords: mobile advertising; place recognition; smartphone sensing; visit detection; visit prediction