Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
552886 | Decision Support Systems | 2008 | 15 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Information Systems
Authors
David Jingjun Xu, Stephen Shaoyi Liao, Qiudan Li,