Limits...
VisitSense: Sensing Place Visit Patterns from Ambient Radio on Smartphones for Targeted Mobile Ads in Shopping Malls.

Kim B, Kang S, Ha JY, Song J - Sensors (Basel) (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

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.

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.

No MeSH data available.


Place recognition accuracy. (a) w.r.t similarity algorithms; (b) w.r.t cutoff threshold; and (c) Wi-Fi scan period.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4541934&req=5

sensors-15-17274-f010: Place recognition accuracy. (a) w.r.t similarity algorithms; (b) w.r.t cutoff threshold; and (c) Wi-Fi scan period.

Mentions: First, we evaluated the effect of using noise-filtered Wi-Fi fingerprinting on the place recognition accuracy. To make sure, we used four similarity measure algorithms. Figure 10a shows the evaluation results. When using noise-filtered Wi-Fi fingerprinting, the place recognition accuracy increased for all the similarity algorithms. For example, when using Pearson correlation coefficient, the accuracy is increased from 68% to 85%. Note that, when comparing two fingerprints with different APs, SensLoc set the RSSI of complemented APs to 0 dBm to make the fingerprints correspond. However, as shown in the figure, this resulted in poor accuracy. Therefore, in VisitSense, we used −120 dBm for complement APs. Using weak RSSI for complement APs is more reasonable.


VisitSense: Sensing Place Visit Patterns from Ambient Radio on Smartphones for Targeted Mobile Ads in Shopping Malls.

Kim B, Kang S, Ha JY, Song J - Sensors (Basel) (2015)

Place recognition accuracy. (a) w.r.t similarity algorithms; (b) w.r.t cutoff threshold; and (c) Wi-Fi scan period.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541934&req=5

sensors-15-17274-f010: Place recognition accuracy. (a) w.r.t similarity algorithms; (b) w.r.t cutoff threshold; and (c) Wi-Fi scan period.
Mentions: First, we evaluated the effect of using noise-filtered Wi-Fi fingerprinting on the place recognition accuracy. To make sure, we used four similarity measure algorithms. Figure 10a shows the evaluation results. When using noise-filtered Wi-Fi fingerprinting, the place recognition accuracy increased for all the similarity algorithms. For example, when using Pearson correlation coefficient, the accuracy is increased from 68% to 85%. Note that, when comparing two fingerprints with different APs, SensLoc set the RSSI of complemented APs to 0 dBm to make the fingerprints correspond. However, as shown in the figure, this resulted in poor accuracy. Therefore, in VisitSense, we used −120 dBm for complement APs. Using weak RSSI for complement APs is more reasonable.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

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.

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.

No MeSH data available.