Combining empirical experimentation and modeling techniques : A design research approach for personalized mobile advertising applications
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 710-724 |
Journal / Publication | Decision Support Systems |
Volume | 44 |
Issue number | 3 |
Online published | 13 Oct 2007 |
Publication status | Published - Feb 2008 |
Link(s)
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.
Research Area(s)
- Bayesian networks, Mobile advertising, Mobile commerce, Personalization, User modeling
Citation Format(s)
Combining empirical experimentation and modeling techniques: A design research approach for personalized mobile advertising applications. / Xu, David Jingjun; Liao, Stephen Shaoyi; Li, Qiudan.
In: Decision Support Systems, Vol. 44, No. 3, 02.2008, p. 710-724.
In: Decision Support Systems, Vol. 44, No. 3, 02.2008, p. 710-724.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review