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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.

In this paper, we discuss our experience in offering a usability course with projects taken from an active open source software development project. We describe what was done in the class inside the larger context of the usability of open source software. We conclude with an invitation for others to adopt this model and use it for their own purposes.

Abstract. Consumer research has indicated that consumers use compensatory and non-compensatory decision strategies when formulating their purchasing decisions. Compensatory decision-making strategies are used when the consumer fully rationalizes their decision outcome whereas non-compensatory decision-making strategies are used when the consumer considers only that information which has most meaning to them at the time of decision. When designing online shopping support tools, incorporating these decision-making strategies with the goal of personalizing the design of the user interface may enhance the overall quality and satisfaction of the consumer's shopping experiences. This paper presents work towards this goal. The authors describe research that refines a previously developed procedure, using techniques in cluster analysis and rough sets, to obtain consumer information needed in support of designing customizable and personalized user interface enhancements. The authors further refine their procedure by examining and evaluating techniques in traditional association mining, specifically conducting experimentation using the Eclat algorithm for use with the authors' previous work. A summary discussing previous work in relation to the new evaluation is provided. Results are analyzed and opportunities for future work are described.