%0 Journal Article
%T Effort of User-Item Degree Corrlations to Bipartite Network Personalized Recommendations<br>用户和项目联合度对二分网络个性化推荐的影响
%A CHENG Ting-ting
%A WANG Heng-shan
%A LIU-Jian-guo
%A <br>程婷婷
%A 王恒山
%A 刘建国
%J 计算机科学
%D 2011
%I 
%X In this paper first bipartite graph was project based on mass diffusion, then random walk method was used to get collaborative filtering results. Degree correlation between users and objects was embedded into the similarity index to improve the algorithm The numerical simulation shows that the algorithmic accuracy of the presented algorithm is improved by 18. 19% in the optimal case and the diversity is improved by 21. 90%. The statistical analysis on the prodtrct distribution of the user and object degrees indicates that, in the optimal case, the distribution obeys the power-law and the exponential is equal to--2. 33.
%K Recommendation systems
%K I3ipartite network
%K Collaborative filtering<br>个性化推荐，二分网络，协同过滤
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=B5A8D881FE7BC1F548175C4520B94DDB&yid=9377ED8094509821&vid=16D8618C6164A3ED&iid=94C357A881DFC066&sid=CEC789B3C68C3BB3&eid=D5C73DEF4CF8FAF3&journal_id=1002-137X&journal_name=计算机科学&referenced_num=0&reference_num=13