面向个性化推荐系统的二分网络协同过滤算法研究  

Research on collaborative filtering algorithm of bipartite network oriented to personal recommendation system

作　　者：李霞[1] 李守伟[2] 

机构地区：[1]滨州医学院网络中心,山东滨州256603 [2]江苏大学管理学院,江苏镇江212013

出　　处：《计算机应用研究》2013年第7期1946-1949,共4页Application Research of Computers

基　　金：滨州市科技计划资助项目(2011ZC1002);国家社会科学基金一般项目(11BJL074);国家教育部人文社会科学研究规划基金资助项目(10YJAZH042);江苏省高校哲学社会科学研究基金资助项目(2010SJB630010)

摘　　要：为提高个性化推荐系统的推荐效率和准确性,提出了个性化推荐系统的二分网络协同过滤算法。协同过滤算法引入二分网络描述个性化推荐系统,使用灰色关联度来度量用户相似性和项目相似性,对灰色关联相似度加权求和预测用户对项目的预测打分值,从而提供给用户排序后的项目列表。实验结果表明,协同过滤算法有效提高了过滤推荐的精准度和可靠性,具有良好的推荐效果。In order to improve the recommendation efficiency and accuracy of personalized recommendation system,this paper presented a collaborative filtering algorithm based on bipartite network for personalized recommendation system.The collaborative filtering algorithm described personal recommendation system using bipartite network,and used grey relationship degree to measure user similarity and object similarity.It forecasted the object score of user evaluation with similarity-weighted of grey relationship degree,and then provided ordered object list to every user.Experimental results show that the collaborative filtering algorithm can effectively resolve above problems,and it is higher accuracy and reliability and better recommendation results.

关 键 词：个性化推荐 协同过滤 二分网络 灰色关联 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]