In user-based collaborative filtering algorithm, the nearest neighbors of the target user are not accurate and reliable due to the tendency of userˊs rating and the sparsity of rating matrix. An effective algorithm is presented to obtain userˊs nearest neighbors. Firstly, the definitions of positive and negative ratings for user group are given respectively, and the nearest neighbors of target user are selected from the group containing same rating tendency. Then, the nearest neighbors of target user with few common rating items and high similarity are corrected. Thus, the final nearest neighbor collection is obtained. Experimental results show that the modified algorithm of neighbor selection improves the recommended quality effectively to some extent.%在基于用户的协同过滤算法中，用户评分倾向性和评分矩阵的稀疏性致使难以准确可靠地搜寻目标用户的近邻。基于此，文中提出基于目标用户近邻修正的协同过滤算法。首先定义积极评分和消极评分两类用户群体，选择从目标用户评分倾向性一致的用户群体中寻找其近邻。然后对与目标用户共同评分项数量少而相似度可能高的近邻进行修正，为目标用户寻找更准确的近邻集合。实验表明，文中算法在一定程度上能有效提高推荐质量。
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