关键词: 协同过滤, 相似性, 皮尔逊相关系数, 平均绝对误差（MAE）

Abstract: The similarity measure between users has significant impact on the results of collaborative filtering recommendation system. To increase the accuracy of neighbor selection, a weighted Pearson Correlation Coefficient（PCC） similarity measurement is proposed to calculate PCC weighting factor directly with the number of user-item ratings. The improved pearson similarity metrics is applied to empirical analysis of the MovieLens, Douban and Epinions dataset. Experimental results show that the proposed method can improve the recommendation accuracy of collaborative filtering effectively in terms of Mean Absolute Error（MAE） and precision.

Key words: collaborative filtering, similarity, pearson correlation coefficient；Mean Absolute Error（MAE）