Abstract
RFID technology has been recently adopted in retail environments to track consumer in-store movements, bringing about new exciting opportunities for spatial data mining-enabled marketing. In this paper, we propose a Dynamic Bayesian Networks (DBN)-based model of customer in-store shopping trips and activities. This model infers a customer's product purchase interest given the observations of customer in-store movement data collected through a RFID-based wireless network. We also report a preliminary evaluation of our approach using a real-world dataset. Our proposed approach can be potentially used to help create an intelligent shopping environment, in which store operators can target their marketing efforts at providing effective location-aware real-time product recommendation for individual customers.
Original language | English (US) |
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Pages | 55-60 |
Number of pages | 6 |
State | Published - Jan 1 2009 |
Event | 19th Workshop on Information Technologies and Systems, WITS 2009 - Phoenix, AZ, United States Duration: Dec 14 2009 → Dec 15 2009 |
Other
Other | 19th Workshop on Information Technologies and Systems, WITS 2009 |
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Country | United States |
City | Phoenix, AZ |
Period | 12/14/09 → 12/15/09 |
Keywords
- DBN
- Dynamic bayesian networks
- Radio frequency identification devices
- Retailing
- RFID
- Shopping activity modeling
ASJC Scopus subject areas
- Information Systems
- Control and Systems Engineering