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.


An example of a stable AP list (SAL).
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-17274-f007: An example of a stable AP list (SAL).

Mentions: VisitSense detects two changes: ambient radio stability change and user mobility change. First, to accurately detect an ambient radio stability change, we introduce a Stable AP List (SAL). A SAL is an ordered list of ambient APs that (1) continuously appears in a Wi-Fi scan window (e.g., response ratio: 1) and (2) shows strong RSSI (e.g., cutoff RSSI threshold: −90 dBm). A SAL is sorted in descending order of RSSI. The RSSI of SAL is an averaged value over a scan window. In Figure 7, an example of a SAL is illustrated.


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)

An example of a stable AP list (SAL).
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-17274-f007: An example of a stable AP list (SAL).
Mentions: VisitSense detects two changes: ambient radio stability change and user mobility change. First, to accurately detect an ambient radio stability change, we introduce a Stable AP List (SAL). A SAL is an ordered list of ambient APs that (1) continuously appears in a Wi-Fi scan window (e.g., response ratio: 1) and (2) shows strong RSSI (e.g., cutoff RSSI threshold: −90 dBm). A SAL is sorted in descending order of RSSI. The RSSI of SAL is an averaged value over a scan window. In Figure 7, an example of a SAL is illustrated.

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.