%0 Journal Article
%T Hybrid collaborative filtering algorithm based on KNN-SVM<br>基于KNN-SVM的混合协同过滤推荐算法
%A LV Cheng-shu
%A WANG Wei-guo
%A DING Yong-jian
%A <br>吕成戍
%A 王维国
%A 丁永健
%J 计算机应用研究
%D 2012
%I 
%X The problem of data sparseness has great influence on collaborative filtering recommendation system's accuracy, balance for this missing data fusion method, this paper proposed a hybrid collaborative filtering algorithms based on KNN-SVM. K-nearest neighbor method used the training set to fill the missing data, and then cross-validated by SVM classification. Comprehend advantages both KNN and SVM in order to overcome impact of data quality on the recommended algorithm. The proposed approach was applied to benchmark problems, and the simulation results show it is valid.
%K data sparsity
%K support vector machine(SVM)
%K K-nearest neighbor
%K collaborative filtering<br>数据稀疏性
%K 支持向量机
%K K-最近邻
%K 协同过滤
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=F886A295C1E57CC5116E1AF657793EA4&yid=99E9153A83D4CB11&vid=771469D9D58C34FF&iid=94C357A881DFC066&sid=F69F61A42EF5D746&eid=E8941A3800A46ABD&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=15