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Consumer purchasing behavior extraction using statistical learning theory

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journal contribution
posted on 06.12.2017, 00:00 by Y Zuo, A B M Shawkat Ali, K Yada
Consumers classification is one of the most important task in the retail sector. RFID (Radio Frequency IDentification) - A wireless non-contact technology is made easier to classify the consumers’ in-store behavior, recently. This paper presents an extraction of consumer purchasing behavior using statistical learning theory SVM (Support Vector Machine). In this research, we present our recent investigation outcome on the consumers shopping behavior in a Japanese supermarket using RFID data. We observe that it is possible to express the individual difference of consumers how are they spending time (we call it stay time in this paper) on shopping in a certain area of the supermarket. The contribution of this research is in two folds: we employ a SVM model on dealing with the RFID data of the consumer in-store behaviour firstly, as compared with other forecast model such as linear regression analysis and bayesian network, SVM provides a significant improvement in the forecasting accuracy of purchase behaviour (from 81.49%to 88.18%). Secondly, the kernel trick is adopted inside the SVM theory to choose the appropriate kernel for consumer purchasingbehavior extraction.

Funding

Other

History

Volume

35

Start Page

1464

End Page

1473

Number of Pages

10

ISSN

1877-0509

Location

Netherlands

Publisher

Elsevier

Language

en-aus

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Kansai Daigaku; School of Engineering and Technology (2013- ); TBA Research Institute;

Era Eligible

Yes

Journal

Procedia computer science.