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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.


System architecture of VisitSense.
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sensors-15-17274-f001: System architecture of VisitSense.

Mentions: Figure 1 shows the system architecture of VisitSense. VisitSense consists of three main components: Visit Detector, Place Recognizer, and Visit Predictor. In addition to these, there are sensing components such as Wi-Fi Scanner and Accelerometer Reader. The Visit Detector is responsible for detecting a smartphone user’s entrance and departure from a place by monitoring Wi-Fi scans and accelerometer values. The Place Recognizer is responsible for recognizing the visit place by comparing the sensed Wi-Fi fingerprint to the predefined Wi-Fi fingerprints of the place. It maintains a Wi-Fi fingerprint database to match the fingerprints. The Wi-Fi fingerprint database for diverse shopping malls can be downloaded from a fingerprint repository. The Visit Predictor is responsible for predicting the user’s next visit place by evaluating the probability from the trained probabilistic prediction model. The trained model for diverse shopping malls can be downloaded from a model repository.


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)

System architecture of VisitSense.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-17274-f001: System architecture of VisitSense.
Mentions: Figure 1 shows the system architecture of VisitSense. VisitSense consists of three main components: Visit Detector, Place Recognizer, and Visit Predictor. In addition to these, there are sensing components such as Wi-Fi Scanner and Accelerometer Reader. The Visit Detector is responsible for detecting a smartphone user’s entrance and departure from a place by monitoring Wi-Fi scans and accelerometer values. The Place Recognizer is responsible for recognizing the visit place by comparing the sensed Wi-Fi fingerprint to the predefined Wi-Fi fingerprints of the place. It maintains a Wi-Fi fingerprint database to match the fingerprints. The Wi-Fi fingerprint database for diverse shopping malls can be downloaded from a fingerprint repository. The Visit Predictor is responsible for predicting the user’s next visit place by evaluating the probability from the trained probabilistic prediction model. The trained model for diverse shopping malls can be downloaded from a model repository.

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.