%0 Journal Article
%T Collaborative Filtering Recommendation Algorithm Based on Item Clustering and Global Similarity<br>基于项目聚类的全局最近邻的协同过滤算法
%A 韦素云
%A 业宁
%A 朱健
%A 黄霞
%A 张硕
%J 计算机科学
%D 2012
%I 
%X Abstract When facing with the extreme sparsity of user rating data, traditional similarity measure method performs poor work which results in poor recommendation duality. To address the matter, a new collaborative filtering recommen- elation algorithm based on item clustering and global nearest neighbor set was proposed. Clustering algorithm is applied to cluster items into several classes based on the similarity of the items, and then the local user similarity is calculated in each cluster, at last a newly global similarity between nearest neighbor users is used to measure user similarity. In addi- tion,the factor of overlap is introduced to optimize the accuracy of the local similarity between users. The experimental results show that this algorithm can improve the accuracy of the prediction and enhance the recommendation quality, which shows good result on the condition of the extreme sparse data.
%K Recommendation systems
%K Collaborative filtering
%K Clustering
%K Ulobe similarity
%K Overlap<br>推荐系统，协同过滤，聚类，全局相似性，重叠度因子
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=38EE2F0E8240D8875339E179626A6C28&yid=99E9153A83D4CB11&vid=7C3A4C1EE6A45749&iid=59906B3B2830C2C5&sid=D59111839E7C8BDF&eid=0B4F496D54044D86&journal_id=1002-137X&journal_name=计算机科学&referenced_num=0&reference_num=0