Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Activity-based serendipitous recommendations with the Magitti mobile leisure guide
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
On using existing time-use study data for ubiquitous computing applications
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Enhanced shopping: a dynamic map in a retail store
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Who will be the customer?: a social robot that anticipates people's behavior from their trajectories
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Mining user similarity based on location history
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Why We Buy: The Science of Shopping--Updated and Revised for the Internet, the Global Consumer, and Beyond
SurroundSense: mobile phone localization via ambience fingerprinting
Proceedings of the 15th annual international conference on Mobile computing and networking
Discovering semantically meaningful places from pervasive RF-beacons
Proceedings of the 11th international conference on Ubiquitous computing
SensLoc: sensing everyday places and paths using less energy
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
Video analytics solution for tracking customer locations in retail shopping malls
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning and recognizing the places we go
UbiComp'05 Proceedings of the 7th international conference on Ubiquitous Computing
AdNext: a visit-pattern-aware mobile advertising system for urban commercial complexes
Proceedings of the 12th Workshop on Mobile Computing Systems and Applications
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This paper presents a novel customer malling behavior modeling framework for an urban shopping mall. As an automated computing framework using smartphones, it is designed to provide comprehensive understanding of customer behavior. We prototype the framework in a real-world urban shopping mall. Development consists of three steps; customer data collection, customer trace extraction, and behavior model analysis. We extract customer traces from a collection of 701-hour sensor data from 195 in-situ customers who installed our logging application at Android Market. The practical behavior model is created from the real traces. It has a multi-level structure to provide the holistic understanding of customer behavior from physical movement to service semantics. As far as we know, it is the first work to understand complex customer malling behavior in offline shopping malls.