  In this paper we propose an approach to preference elicitation that is
suitable to large configuration spaces beyond the reach of existing
state-of-the-art approaches. Our setwise max-margin method can be viewed as a
generalization of max-margin learning to sets, and can produce a set of
"diverse" items that can be used to ask informative queries to the user.
Moreover, the approach can encourage sparsity in the parameter space, in order
to favor the assessment of utility towards combinations of weights that
concentrate on just few features. We present a mixed integer linear programming
formulation and show how our approach compares favourably with Bayesian
preference elicitation alternatives and easily scales to realistic datasets.
