A Collaborative Method to Reduce the Running Time and Accelerate the k-Nearest Neighbors Search

DOI reference number: 10.18293/SEKE2015-153 Abstract—Recommendation systems are software tools and techniques that provide customized content to users. The collaborative filtering is one of the most prominent approaches in the recommendation area. Among the collaborative algorithms, one of the most popular is the k-Nearest Neighbors (kNN) which is an instance-based learning method. The kNN generates recommendations based on the ratings of the most similar users (nearest neighbors) to the target one. Despite being quite effective, the algorithm performance drops while running on large datasets. We propose a method, called Restricted Space kNN that is based on the restriction of the neighbors search space through a fast and efficient heuristic. The heuristic builds the new search space from the most active users. As a result, we found that using only 15% of the original search space the proposed method generated recommendations almost as accurate as the standard kNN, but with almost 58% less running time.