第23届IEEE International Conference on Web Services (IEEE ICWS 2016) 于2016年6月27日至7月2日在美国旧金山召开。实验室发表论文《User Preference Based Link Inference for Social Network》并由研究生许浩然作为代表在会议上作了20分钟的论文口头报告。

该文章主要针对社会网络中的链接预测问题，引入用户社交行为中的隐含信息，分别针对用户的属性、好友关系、发布内容及互动行为进行分析与建模，并采用联合模型与融合模型将各分析结果进行结合，提升了链接预测的准确度。

Abstract—In this paper, we focus on the link predication problem in social networks. Our approach is based on the observation that there is a large amount of social behavior taking place every day which contains substantial information about user intrinsic characteristics that influence the dynamics of social networks. In order to obtain a deeper understanding of user behavior, we introduce the concept of latent factor to capture the motivation behind social activities. Since user relationships are often asymmetric, we also take into account bilateral user wishes with respect to friend as preferences, which is beyond traditional approaches or overall measurements. Two combination modes are proposed, independent fusion and interdependent fusion, to integrate these hybrid metrics with traditional measurements for link inference. In order to quantify the sensitivity of each element in metrics we use information theory. Experimental results on several real datasets show that our approach has better performance than previous methods.