Integrating a weighted-average method into the random walk framework to generate individual friend recommendations  

作　　者：Jibing GONG Xiaoxia GAO Hong CHENG Jihui LIU Yanqing SONG Mantang ZHANG Yi ZHAO 

机构地区：[1]School of Information Science and Engineering, Yanshan University [2]The Key Lab for Computer Virtual Technology and System Integration,Yanshan University [3]State Key Lab of Mathematical Engineering and Advanced Computing [4]Key Laboratory for Software Engineering of Hebei Province, Yanshan University [5]Department of Systems Engineering and Engineering Management,The Chinese University of Hong Kong

出　　处：《Science China(Information Sciences)》2017年第11期39-60,共22页中国科学（信息科学）（英文版）

基　　金：supported by National High Technology Research and Development Program of China (863 Program) (Grant No. 2015AA124102);Hebei Natural Science Foundation of China (Grant No. F2015203280);Open Project Program of the State Key Laboratory of Mathematical Engineering and Advanced Computing (Grant No. 214125);National Natural Science Foundation of China (Grant No. 61303130);Graduate Innovation Funded Program of Yanshan University (Grant No. 2017XJSS028);Innovation Zone Project Program for Science and Technology of China’s National Defense (Grand No. 2017-0001-863015-0009)

摘　　要：Friend recommendation is a fundamental service in both social networks and practical applications, and is influenced by user behaviors such as interactions, interests, and activities. In this study, we first conduct in-depth investigations on factors that affect recommendation results. Next, we design Friend＋＋, a hybrid multi-individual recommendation model that integrates a weighted average method （WAM） into the random walk （RW） framework by seamlessly employing social ties, behavior context, and personal information. In Friend＋＋, the first plus signifies recommending a new friend through network features, while the second plus stands for using node features. To verify our method, we conduct experiments on three social datasets crawled from the Sina microblog system （Weibo）. Experimental results show that the proposed method significantly outperforms six baseline methods in terms of recall, precision, Fl-measure, and MAP. As a final step, we describe a case study that demonstrates the scalability and universality of our method. Through discussion, we reach a meaningful conclusion： although common interests are more important than user activities in making recommendations, user interactions may be the most important factor in finding the most appropriate potential friends.Friend recommendation is a fundamental service in both social networks and practical applications, and is influenced by user behaviors such as interactions, interests, and activities. In this study, we first conduct in-depth investigations on factors that affect recommendation results. Next, we design Friend＋＋, a hybrid multi-individual recommendation model that integrates a weighted average method （WAM） into the random walk （RW） framework by seamlessly employing social ties, behavior context, and personal information. In Friend＋＋, the first plus signifies recommending a new friend through network features, while the second plus stands for using node features. To verify our method, we conduct experiments on three social datasets crawled from the Sina microblog system （Weibo）. Experimental results show that the proposed method significantly outperforms six baseline methods in terms of recall, precision, Fl-measure, and MAP. As a final step, we describe a case study that demonstrates the scalability and universality of our method. Through discussion, we reach a meaningful conclusion： although common interests are more important than user activities in making recommendations, user interactions may be the most important factor in finding the most appropriate potential friends.

关 键 词：multi-individual friend recommendation architecture behavior context analysis Intimacy degree random walk framework social networks 

分 类 号：TP391.3[自动化与计算机技术—计算机应用技术]