  We consider interactive tools that help users search for their most preferred
item in a large collection of options. In particular, we examine
example-critiquing, a technique for enabling users to incrementally construct
preference models by critiquing example options that are presented to them. We
present novel techniques for improving the example-critiquing technology by
adding suggestions to its displayed options. Such suggestions are calculated
based on an analysis of users current preference model and their potential
hidden preferences. We evaluate the performance of our model-based suggestion
techniques with both synthetic and real users. Results show that such
suggestions are highly attractive to users and can stimulate them to express
more preferences to improve the chance of identifying their most preferred item
by up to 78%.
