Efficient modelling of individual consumer preferences: facilitating agent-based online markets
by Frank A. LoPinto, Cliff T. Ragsdale
International Journal of Electronic Marketing and Retailing (IJEMR), Vol. 3, No. 1, 2010

Abstract: As business-to-consumer e-commerce matures, better tools are needed to help consumers locate products they desire. Currently, user-driven searching, sorting and filtering are widely employed to align consumers and products. These processes could be improved by a quick and easy way to capture and model individual consumer preferences for particular product attributes. We present such a methodology using conjoint analysis and neural networks. This paper presents a study where preferences are elicited using a survey instrument, models of individual participants' preferences are constructed, and prediction accuracy is reported. This proposed methodology performs well for individuals in the example explored here.

Online publication date: Sun, 20-Dec-2009

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Electronic Marketing and Retailing (IJEMR):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com