Abstract In user-based collaborative filtering algorithm, the nearest neighbors of the target user are not accurate and reliable due to the tendency of user′s rating and the sparsity of rating matrix. An effective algorithm is presented to obtain user′s 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.
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2015, Vol. 28 
