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) |
|---|---|
| Title of host publication | 19th Workshop on Information Technologies and Systems, WITS 2009 |
| Publisher | Social Science Research Network |
| Pages | 55-60 |
| Number of pages | 6 |
| State | Published - 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 |
|---|---|
| Country | United States |
| City | Phoenix, AZ |
| Period | 12/14/09 → 12/15/09 |
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Keywords
- DBN
- Dynamic bayesian networks
- Radio frequency identification devices
- Retailing
- RFID
- Shopping activity modeling
ASJC Scopus subject areas
- Information Systems
- Control and Systems Engineering
Cite this
In-store shopping activity modeling based on dynamic bayesian networks. / Yan, Ping; Zeng, Dajun.
19th Workshop on Information Technologies and Systems, WITS 2009. Social Science Research Network, 2009. p. 55-60.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - In-store shopping activity modeling based on dynamic bayesian networks
AU - Yan, Ping
AU - Zeng, Dajun
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
KW - DBN
KW - Dynamic bayesian networks
KW - Radio frequency identification devices
KW - Retailing
KW - RFID
KW - Shopping activity modeling
UR - http://www.scopus.com/inward/record.url?scp=84903832825&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84903832825&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84903832825
SP - 55
EP - 60
BT - 19th Workshop on Information Technologies and Systems, WITS 2009
PB - Social Science Research Network
ER -