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.


User study results (a) the average number of delivered ads; (b) the average rating of delivered ads.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-17274-f014: User study results (a) the average number of delivered ads; (b) the average rating of delivered ads.

Mentions: Figure 14a shows the average number of ads each participant received during the experiment. Each participant received an average of 33.66 ads from LBA, and an average of 26.6 ads from VAA. That is, participants received about 26.3% more ads from LBA than VAA. This is mainly because in the case of VAA, participants receive ads only when they leave a place. Instead, in LBA, participants unnecessarily receive many ads when they simply pass by a place. Figure 14b shows the average rating the participants gave to each ad. The average rating was 2.13 for LBA, and 2.50 for VAA. This indicates participants thought VAA ads were more useful than LBA ads. Through this user study, we can empirically conclude that VAA can provide more useful ads for users while sending fewer ads. This means advertisers can increase the effectiveness of their campaign while reducing the cost to send ads.


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)

User study results (a) the average number of delivered ads; (b) the average rating of delivered ads.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-17274-f014: User study results (a) the average number of delivered ads; (b) the average rating of delivered ads.
Mentions: Figure 14a shows the average number of ads each participant received during the experiment. Each participant received an average of 33.66 ads from LBA, and an average of 26.6 ads from VAA. That is, participants received about 26.3% more ads from LBA than VAA. This is mainly because in the case of VAA, participants receive ads only when they leave a place. Instead, in LBA, participants unnecessarily receive many ads when they simply pass by a place. Figure 14b shows the average rating the participants gave to each ad. The average rating was 2.13 for LBA, and 2.50 for VAA. This indicates participants thought VAA ads were more useful than LBA ads. Through this user study, we can empirically conclude that VAA can provide more useful ads for users while sending fewer ads. This means advertisers can increase the effectiveness of their campaign while reducing the cost to send ads.

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.