Effect of Clustering Algorithm in K-nearest-neighborhood Based Collaborative Filtering
Recommender system in E-commerce analyzes preference of users and items recommendations usually rely on Collaborative Filtering(CF).A classical approach to CF is based on K-Nearest-Neighborhood(KNN) model.But it never consider the classes-relevancy between the samples and the classes their belong.Because of the different contribution to classes in sample set,it′s necessary to discriminate every nearest neighborhood.This paper presents an improved KNN algorithm and verifies its effectiveness by experiments.Through experimental results,the improved CF method can consistently achieve better prediction accuracy than traditional CF algorithm.