信息推荐系统中的朋友关系预测算法设计  

Friendship prediction in recommender system

作　　者：马建威[1] 徐浩[1] 陈洪辉[1] 

机构地区：[1]国防科技大学信息系统工程重点实验室,湖南长沙410073

出　　处：《国防科技大学学报》2013年第1期163-168,共6页Journal of National University of Defense Technology

基　　金：国家自然科学基金项目(60903206,61070216,71071160);国家部委资助项目;国防科技大学研究生创新资助项目(B110502);湖南省研究生创新资助项目(CX2011024)

摘　　要：伴随着互联网规模的不断扩展,信息过载问题越来越突出。信息推荐系统被视为解决信息过载问题的最有效方法。然而目前的方法大多数仅考虑用户独立的反馈,而忽略用户的社会属性对推荐的重要作用,这对信息推荐系统的性能会造成巨大的影响。为此,本文提出了基于朋友关系预测的信息推荐算法,将用户的社会关系预测引入信息推荐过程中,分别基于用户的拓扑信息及历史交互信息建立用户社会关系的存在性判定及关系类型判定,并利用线性回归分析方法和逻辑回归分析方法实现了基本特征的融合。最后,通过在Epinions和Slashdot真实数据集上的实验证明,本方法能够有效提高用户社会关系预测的准确性。Abstrad： As the fast development of the Internet scale, ＂data overload＂ has become one of the most critical problems in computer network analysis. Recommender system has been regarded as the most effective method to solve the problem. But most of existing methods just consider the independent feedback of users without considering the relationship between users, which will inevitably decrease the performance of recommender system. Thus, a friendship prediction algorithm for recommender system was proposed to predict the relationship between different users. Firstly the topological and historical interaction information was taken as the features to judge the existence and relationship type of links. Then the feature combination process based on linear regression algorithm and logistic regression algorithm was implemented. Finally, the experiments based on the real data sets of Epinions and Slashdot were implemented. The experiment results show that our approaches perform very well in link prediction problem. Key words： recommender system; friendship relationship; social network; algorithm design; MAE （mean absolute error）

关 键 词：信息推荐 朋友关系 社会网络 算法设计 MAE(平均绝对误差) 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]