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.

Agatha: Predicting Daily Activities from Place Visit History for Activity-Aware Mobile Services in Smart Cities

“…WhereNext [23] predicts the next location of moving objects by matching a new GPS trajectory to a set of similar GPS trajectories. NextPlace [24] sequential place visit patterns in a large shopping mall, VisitSense [25] provides a probabilistic prediction model based on Bayesian networks. Different from location prediction, we investigate activity prediction.…”

“…NextPlace [24] presents a prediction model based on nonlinear time series analysis in order to predict not only the next location but also the arrival and residence time of a mobile user. Exploiting frequent sequential place visit patterns in a large shopping mall, VisitSense [25] provides a probabilistic prediction model based on Bayesian networks. Different from location prediction, we investigate activity prediction.…”

Next Place Prediction Based on Spatiotemporal Pattern Mining of Mobile Device Logs

“…When the level of geographical granularity for prediction comes into consideration, a more precise level is desired to enable further sophisticated services. Through discovering the next places at the level of users’ daily lives, such as local shops and school cafeterias, various customized applications can be enabled, including recommendation of tailored information, such as automated reservation and personalized advertisements [ 6 , 7 , 8 ].…”

Sensors for Indoor Mapping and Navigation

“…The simultaneous map generation and positioning methods in [ 14 , 15 ] combine inertial sensors with camera images. In [ 16 ] accelerometer data and ambient radio sensed by a smartphone are used to detect place visits of the user. The pressure sensor built in smartphones (usually referred to as barometer) is used to estimate the vertical position or to recognize vertical movements in [ 7 , 10 , 13 , 17 , 18 ].…”