Combining empirical experimentation and modeling techniques: A design research approach for personalized mobile advertising applications

Authors:
David Jingjun Xu;Stephen Shaoyi Liao;Qiudan Li
Affiliations:
Sauder School of Business, University of British Columbia, Canada;Department of Information Systems, City University of Hong Kong, Hong Kong and School of Economics and Management, South West JiaoTong University, PR China;Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, PR China
Venue:
Decision Support Systems
Year:
2008

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Abstract

We propose a design research approach combining behaviour and engineering techniques to better support user modeling in personalized mobile advertising applications. User modeling is a practical means of enabling personalization; one important method to construct user models is that of Bayesian networks. To identify the Bayesian network structure variables and the prior probabilities, empirical experimentation is adopted and context, content, and user preferences are the salient variables. User data collected from the survey are used to set the prior probabilities for the Bayesian network. Experimental evaluation of the system shows it is effective in improving user attitude toward mobile advertising.