Fast Collaborative Filtering with a k-Nearest Neighbor Graph


Title Fast Collaborative Filtering with a k-Nearest Neighbor Graph
Authors Youngki Park, Sungchan Park, Sang-goo Lee, Woosung Jung
Year 2014 / 1
Keywords Fast Collaborative Filtering, real-time recommendation, k-nearest neighbor graph, Greedy Filtering
Acknowledgement NRF
Publication Type International Conference
Publication Proceedings of the International Conference on Big Data and Smart Computing (BigComp 2014), pp. 92-95
Link url


Traditional user-based/item-based Collaborative Filtering algorithms predict the preferences of all of the unseen items of a user. While this approach facilitates evaluations of the accuracy of various algorithms using the root mean square error, it consumes a considerable amount of time to recommend items for users. In this paper, we present a fast Collaborative Filtering algorithm using a k-nearest neighbor graph. Not only does this algorithm predict the preferences of only the k-nearest neighbor items, but it also shortens the execution time by calculating a knearest neighbor item graph in less time based on greedy filtering. The experimental results show that our approach outperforms traditional user-based/item-based Collaborative Filtering algorithms in terms of both the preprocessing time and the query processing time without sacrificing the level of accuracy.