Active Learning for Matching Problems
  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 
Laurent Charlin (University of Toronto), Rich Zemel (University of   Toronto), Craig Boutilier (University of Toronto)
cs.LG cs.AI cs.IR