In-store shopping activity modeling based on dynamic bayesian networks

Ping Yan, Daniel D. Zeng

Research output: Contribution to conferencePaper

3 Scopus citations

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 languageEnglish (US)
Pages55-60
Number of pages6
StatePublished - Jan 1 2009
Event19th Workshop on Information Technologies and Systems, WITS 2009 - Phoenix, AZ, United States
Duration: Dec 14 2009Dec 15 2009

Other

Other19th Workshop on Information Technologies and Systems, WITS 2009
CountryUnited States
CityPhoenix, AZ
Period12/14/0912/15/09

Keywords

  • DBN
  • Dynamic bayesian networks
  • Radio frequency identification devices
  • Retailing
  • RFID
  • Shopping activity modeling

ASJC Scopus subject areas

  • Information Systems
  • Control and Systems Engineering

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  • Cite this

    Yan, P., & Zeng, D. D. (2009). In-store shopping activity modeling based on dynamic bayesian networks. 55-60. Paper presented at 19th Workshop on Information Technologies and Systems, WITS 2009, Phoenix, AZ, United States.