In multi-criteria decision making problems such as selecting development policies, selecting software products, or searching for commodities to purchase, it is necessary to have a precise model of the user preferences. Studies have revealed that often people are unable to state their preferences up front, and that they start to evaluate solution alternatives with a small set of high-value preferences; but change the value of those preferences as they discovery other solution features which they can incorporate into their preference models (B. Faltings et al., 2004). While, a variety of preference elicitation models have been proposed, limited or no effort has been made to utilize historical data to provide decision support for the elicitation of user preferences. In this paper, we discuss using neural net to take advantage of historical data, and provide decision support for developing user preference models, as well as preference value functions; from a set of high-value preferences. Moreover, we report results of using our technique to elicit the user preferences for evaluating and selecting a commercial-off-the-shelf software component
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