计算机工程与应用2016，Vol.52Issue(22)：222-225,259,5.DOI:10.3778/j.issn.1002-8331.1501-0181
基于加权相似度的用户协同过滤方法
Improved user-based collaborative filtering method based on weighted similarity
摘要
Abstract
The similarity measure between users has significant impact on the results of collaborative filtering recommen-dation 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.关键词
协同过滤/相似性/皮尔逊相关系数/平均绝对误差(MAE)Key words
collaborative filtering/similarity/pearson correlation coefficient/Mean Absolute Error(MAE)分类
信息技术与安全科学引用本文复制引用
范永全,杜亚军..基于加权相似度的用户协同过滤方法[J].计算机工程与应用,2016,52(22):222-225,259,5.基金项目
教育部春晖计划（No.Z2011088）；四川省教育厅重点项目（No.11ZB002）；四川省高校重点实验室基金（No.SZJJ2012-027， No.SZJJ2014-033）；西华大学重点科研基金项目（No.Z1412620）。 （）
