%0 Journal Article
%T Collaborative filtering recommendation model based on effective dimension reduction and K-means clustering<br>一种结合有效降维和K-means聚类的协同过滤推荐模型*
%A YU Xue
%A LI Min-qiang
%A <br>郁雪
%A 李敏强
%J 计算机应用研究
%D 2009
%I 
%X To address the curse of dimensionality,this paper proposed a new hybrid recommendation model which imposed principal components analysis technique combined with K-means clustering. In the approach, the clusters generated from the relatively low dimension vector space transformed by PCA step, and then used for neighborhood selection in order to alternate the exiting K-nearest neighbor searching in high dimensions. The experiment results indicate that the proposed model can produce better prediction quality and higher efficiency. Especially, when the target visitor with few historic information comes, it performs more robust.
%K collaborative filtering
%K principle components analysis(PCA)
%K dimension reduction
%K K-means clustering<br>协同过滤
%K 主成分分析
%K 维数约简
%K K-means聚类
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=35F609C2FA212B1C4883ACD79C307E1D&yid=DE12191FBD62783C&vid=96C778EE049EE47D&iid=F3090AE9B60B7ED1&sid=C8ECCD17E8755D73&eid=5755EC941FD55281&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=11