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- W81745979 abstract "The quality of the natural environment has become one of the primary concerns in present society. In Canada, we have been asked to take on the One Tonne Challenge to reduce personal household emissions by 1 tonne. However, very little has been done to illuminate the various connections between our household purchases and the effect they can have on the quality of our health and environment. Several decision support systems are available to assist consumers compare alternatives. However, these systems do little to enhance the consumer's experience. Correct clustering of consumers in terms of their product attribute preferences would enable the construction of personalized user interfaces thus increase consumer satisfaction when interacting with the system and increase the chance of inspiring greener purchasing habits. This paper analyzes a clus- tering technique that uses methods from multivariate statistics, rough set theory, and machine learning to cluster users in a web- based environmental decision support system and test the success of the clustering. Results from our analysis are discussed. I. INTRODUCTION The Kyoto accord for reduction of greenhouse gas (GHG) emissions represents a step toward a cleaner environment. Although Canadians have been asked to take on the One Tonne Challenge to reduce personal household emissions by 1 tonne, very little has been done to illuminate the various connections between our household purchases and the effect they can have on the quality of our health and environment. The environmental soundness of many of these purchases goes unquestioned. If we consider a life cycle assessment approach, it becomes clear that many of these purchases have potential to create a negative impact, however indirectly. Several environmental decision support systems (EDSS) are available to assist consumers compare alternatives. However, these systems seldom attempt to enhance the consumer's expe- riences when interacting with the system. Correct clustering of consumers in terms of their product attribute preferences would allow the construction of personalized user interfaces and increase consumer satisfaction by emphasizing those items with attributes that meet their specific values. By allowing consumers to also compare their current choices with possible alternatives, some of which may be more eco-effective 1 , the chance of inspiring greener purchasing habits is great." @default.
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- W81745979 date "2005-01-01" @default.
- W81745979 modified "2023-10-18" @default.
- W81745979 title "Personalizing User Interfaces for Environmental Decision Support Systems" @default.
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