There is significant interest in developing new methods to design more effective user interfaces for decision support tools in online shopping environments. Many online companies have already begun to provide their consumers with enhanced user interface options, such as the ability to customize and/or personalize their user interface. However, for these enhanced options to produce meaningful, useful results, consumers are often required to input substantial amounts of information, placing a strain on the consumers' cognitive decision-making abilities and disrupting their focus on their immediate decision task(s). In this paper, the authors describe a personalization technique to reduce the amount of consumer information required to develop and deploy systems providing these enhanced options. Over the course of the three experiments, the authors built upon each experiment utilizing a combination of traditional statistical methods and rough set theory. This paper will describe the refined technique and the procedures, algorithms, observations, and analysis of the experiments conducted. As well, a discussion detailing future work will be provided.
@inproceedings{2006-11-MacSleHep,
Author = “Maciag, Timothy and '{S}l\k{e}zak, Dominik and Hepting, Daryl H.”,
Title = “Consumer Modelling in Support of Interface Design”,
Url = "https://www2.cs.uregina.ca/~hepting/research/works/2006-11-MacSleHep-Consumer-Modelling-in-Support-of-Interface-Design.html",
Booktitle = “2006 International Conference on Hybrid Information Technology”,
Doi = “10.1109/ICHIT.2006.253605”,
Month = “November”,
Pages = “153–160”,
Volume = “2”,
Year = “2006”,
}