Scalable collaborative filtering based on efficient identification of similar users

User-based collaborative filtering suffers from significant amount of computational overhead to find users similar to a target user. To reduce the overhead, we propose a novel method to identify unnecessary users and items in computing the similarity. Also, we propose a data structure to support the method quite efficiently. Through extensive experiments, we show the proposed method outperforms traditional methods up to 33.8 times.