Update For Collaborative Filtering,Matrix Factorization And Knn Algorithms Applied For Recommendation System

As the booming development of the Internet,people suffer more and more from information overload.How to retrieve useful contents from the enormous information becomes a focusing concern,which brings about the development of recommendation system.This paper mainly introduced some most up to date algorithms applied for recommendation system,including collaborative filtering(CF),singular value decomposition(SVD)and k nearest neighbors(kNN),and made certain adaptions to enhance the performance or accelerate the speed.As for CF,we updated the traditional similarity measurement.We introduced a hybrid similarity function to enhance the performance.Also,K means method was applied for a faster calculation.As for SVD,we introduced demographical information and a structured method to optimize the algorithm.We also attempted a blocked SVD for faster convergence.As for kNN,we renewed the algorithm for row similarity calculation with additional demographical information.Furthermore,we introduced the user and item biases to the algorithm by taking user and item differences into consideration when researching the neighbors,which boosted the performance significantly.