%0 Journal Article
%T Combining Singular Value Decomposition and Neighbor-based Method in Collaborative Filtering<br>在协同过滤中结合奇异值分解与最近邻方法
%A KONG Fan-sheng
%A <br>孙小华
%A 陈洪
%A KONG Fan-sheng
%A 孔繁胜
%J 计算机应用研究
%D 2006
%I 
%X Collaborative filtering is becoming a popular technique for reducing information overload.However,it has three major limitations,accuracy,data sparsity and scalability.We propose a new collaborative filtering algorithm to solve the problem of data sparsity.We utilize the results of singular value decomposition for neighbors selecting,then use the neighborhood-based method to produce the prediction of unrated items.Our experimental results on EachMovie dataset show that the algorithm outperforms the conventional neighborhood-based method and SVD method when the available ratings are sparse.
%K Singular Value Decomposition(SVD)
%K Collaborative Filtering
%K Recommender Systems<br>奇异值分解
%K 协同过滤
%K 推荐系统
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=74EC6CF2D70805A4&yid=37904DC365DD7266&vid=EA389574707BDED3&iid=9CF7A0430CBB2DFD&sid=9F6DA927E843CD50&eid=3D9746C06EC12B45&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=13