Nearest Neighbor Model Recommendation Algorithm Based on LSH and MapReduce

Traditional k-nearest neighborhood (KNN) model has been widely used in the recommender systems. However,with the increasing of users and items,the large scale of similarity between users or items need to be calculated and the time complexity is too high.In this paper,a nearest neighbor model recommendation algorithm combined with a locality sensitive hash (Locality -Sensitive Hashing,LSH) and MapReduce is proposed,which is a way to linear time complexity by parallel computing similarity between users or items,reducing the time and space complexity.Simulate experiments in Tencent Weibo datasets show that the proposed model can effectively solve the problem of high time complexity exists in the traditional nearest neighbor model for large data sets and significantly improve the accuracy of the traditional nearest neighbor model and reduce the time -consuming.