Collaborative Filtering Recommendation Algorithm Based on Neighbor Rating Imputation
Ya-Jun Leng, Changyong Liang, Qing Hua Lu, Lu Wenxing · Jisuanji gongcheng · 2012
Data sparsity influences the recommendation quality of collaborative filtering algorithm.To address this problem,a new hybrid collaborative filtering algorithm based on neighbor rating imputation is proposed.The dimensions of original rating matrix are reduced by Principal Component Analysis(PCA),which can reduce the computational complexity.Singular Value Decomposition(SVD) is used to impute missing ratings of the neighbors,which can alleviate the data sparsity.Experiments are carried out on MovieLens dataset,and the results show that the algorithm has higher the recommendation efficiency.