Combining Singular Value Decomposition and Neighbor-based Method in Collaborative Filtering
在协同过滤中结合奇异值分解与最近邻方法

Collaborative filtering is becoming a popular technique for reducing information overload.However,it has three major limitations,accuracy,data sparsity and scalability.We propose a new collaborative filtering algorithm to solve the problem of data sparsity.We utilize the results of singular value decomposition for neighbors selecting,then use the neighborhood-based method to produce the prediction of unrated items.Our experimental results on EachMovie dataset show that the algorithm outperforms the conventional neighborhood-based method and SVD method when the available ratings are sparse.