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Collaborative Filtering (CF) requires user-rated training examples for statistical inference about the preferences of new users. Active learning strategies identify the most informative set of training examples through minimum interactions with the users. Current active learning approaches in CF make an implicit and unrealistic assumption that a user can provide rating for any queried item. This paper introduces a new approach to the problem which does not make such an assumption. Wedoi:10.1145/1390334.1390352 dblp:conf/sigir/HarpaleY08 fatcat:ew4dqarjprfddhf3omwysfs7yq