Efficient modelling of individual consumer preferences: facilitating agent-based online markets

Frank A. LoPinto and Cliff T. Ragsdale

International Journal of Electronic Marketing and Retailing, 2010, vol. 3, issue 1, 66-81

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.

Keywords: efficient modelling; individual preferences; consumer preferences; preference modelling; conjoint analysis; agent-based systems; multi-agent systems; online markets; neural networks; ANNs; business-to-consumer; B2C; e-commerce; electronic commerce; product attributes. (search for similar items in EconPapers)
Date: 2010
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