Latent Co-interests' Relationship Prediction  

作　　者：Feng Tan Li Li Zheyu Zhang Yunlong Guo 

机构地区：[1]the Department of Computer and Information Science Southwest University

出　　处：《Tsinghua Science and Technology》2013年第4期379-386,共8页清华大学学报（自然科学版（英文版）

基　　金：the National Natural Science Foundation of China (No. 61170192);the Natural Science Foundations of Municipality of Chongqing(No. CSTC2012JJB40012)

摘　　要：With the development of the social media and Internet, discovering latent information from massive information is becoming particularly relevant to improving user experience. Research efforts based on preferences and relationships between users have attracted more and more attention. Predictive problems, such as inferring friend relationship and co-author relationship between users have been explored. However, many such methods are based on analyzing either node features or the network structures separately, few have tried to tackle both of them at the same time. In this paper, in order to discover latent co-interests＇ relationship, we not only consider users＇ attributes but network information as well. In addition, we propose an Interest-based Factor Graph Model （I-FGM） to incorporate these factors. Experiments on two data sets （bookmarking and music network） demonstrate that this predictive method can achieve better results than the other three methods （ANN, NB, and SVM）.With the development of the social media and Internet, discovering latent information from massive information is becoming particularly relevant to improving user experience. Research efforts based on preferences and relationships between users have attracted more and more attention. Predictive problems, such as inferring friend relationship and co-author relationship between users have been explored. However, many such methods are based on analyzing either node features or the network structures separately, few have tried to tackle both of them at the same time. In this paper, in order to discover latent co-interests＇ relationship, we not only consider users＇ attributes but network information as well. In addition, we propose an Interest-based Factor Graph Model （I-FGM） to incorporate these factors. Experiments on two data sets （bookmarking and music network） demonstrate that this predictive method can achieve better results than the other three methods （ANN, NB, and SVM）.

关 键 词：linking prediction node similarity social network factor graph model 

分 类 号：F49[经济管理—产业经济] F274