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Recommender systems play an important role in supporting people getting items they like. One type of recommender systems is userbased collaborative filtering. The fundamental assumption of user-based collaborative filtering is that people who share similar preferences for common items behave similar in the future. The similarity of user preferences is computed globally on common rated items such that partial preference similarities might be missed. Consequently, valuable ratings of partiallydoi:10.5167/uzh-25861 fatcat:otuv23w4affftjtxc36st53t7e