在计算用户相似度时，传统的协同过滤推荐算法往往只考虑单一的用户评分矩阵，而忽视了项目之间的相关性对推荐精度的影响。对此，本文提出了一种优化的协同过滤推荐模型，在用户最近邻计算时引入项目相关性度量方法，以便使得最近邻用户的选择更准确；此外，在预测评分环节考虑到用户兴趣随时间衰减变化，提出了使用衰减函数来提升评价的时间效应的影响。实验结果表明，本文提出的算法在预测准确率和分类准确率方面均优于基于传统相似性度量的项目协同过滤算法。

In most traditional collaborative filtering algorithms, user rating matrix is usually an only important factor in calculating users’ similarity instead of considering the impact of the correlation between items. Therefore, an improved model of collaborative filtering recommendation is presented in this paper. Firstly, a method of item similarity measurement is introduced in the process of computing the user’s nearest neighbor to get more appropriate neighbors. In addition, due to that the users interests will decay over time, time weight is added in the process of computing item ratings．Experimental results show that the proposed algorithm can obtain better performance than other traditional collaborative filtering algorithms in aspects of prediction accuracy and classification accuracy.