DocumentCode :
621678
Title :
A genetic approach to plan shopping in the AmI-based Blended Commerce
Author :
Gaeta, Matteo ; Loia, Vincenzo ; Orciuoli, Francesco ; Parmentola, Maurizio
Author_Institution :
Dipartimento di Ingegneria dell´Informazione, Ingegneria Elettrica e Matematica Applicata, University of Salerno, Fisciano, SA, 84084 Italy
fYear :
2013
fDate :
28-31 May 2013
Firstpage :
1
Lastpage :
6
Abstract :
This work describes an approach to sinergistically exploit Ambient Intelligence, Smartphones of last generation and Genetic Computation in order to support innovative Blended Commerce scenarios. The paper proposes both a framework for AmI-based Blended Commerce and an instantiation of this framework to implement a scenario where a Shopping Mall is presented as an intelligent environment in which customers use the NFC capabilities of their smartphones in order to manage e-coupons that are produced, suggested (also in a context-aware way) and consumed by the same environment. In this scenario, the main function of the intelligent environment is supporting customers to define shopping plans that minimize their total costs by looking for best prices and most convenient discounts for the needed products. The paper proposes a genetic approach to find sub-optimal solutions for the shopping plan problem that is not trivial given that the final cost for a single product of a plan is dependent by the previous purchases because, in a coupon world, every purchase could generate a discount for next purchases.
Keywords :
Ambient intelligence; Biological cells; Business; Context; Genetic algorithms; Service-oriented architecture; Smart phones; Ambient Intelligence; Blended Commerce; Genetic Algorithm; Shopping Plan; e-Couponing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Electronics (ISIE), 2013 IEEE International Symposium on
Conference_Location :
Taipei, Taiwan
ISSN :
2163-5137
Print_ISBN :
978-1-4673-5194-2
Type :
conf
DOI :
10.1109/ISIE.2013.6563733
Filename :
6563733
Link To Document :
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