  Effective learning of user preferences is critical to easing user burden in
various types of matching problems. Equally important is active query selection
to further reduce the amount of preference information users must provide. We
address the problem of active learning of user preferences for matching
problems, introducing a novel method for determining probabilistic matchings,
and developing several new active learning strategies that are sensitive to the
specific matching objective. Experiments with real-world data sets spanning
diverse domains demonstrate that matching-sensitive active learning
