Reversed CF: A fast collaborative filtering algorithm using a k-nearest neighbor graph

User-based and item-based collaborative filtering (CF) methods are two of the most widely used techniques in recommender systems. While these algorithms are widely used in both industry and academia owing to their simplicity and acceptable level of accuracy, they require a considerable amount of time in finding top-k similar neighbors (items or users) to predict user preferences of unrated items. In this paper, we present Reversed CF (RCF), a rapid CF algorithm which utilizes a k-nearest neighbor (k-NN) graph. One main idea of this approach is to reverse the process of finding k neighbors; instead of finding k similar neighbors of unrated items, RCF finds the k-nearest neighbors of rated items. Not only does this algorithm perform fewer predictions while filtering out inaccurate results, but it also enables the use of fast k-NN graph construction algorithms. The experimental results show that our approach outperforms traditional user-based/item-based CF algorithms in terms of both preprocessing time and query processing time without sacrificing the level of accuracy.