来源： 计算机学院 | 发表时间： 2018-03-19 | 浏览次数： 296
题目：Learning User Distance from Multiple Social Networks
How to model user distance from multiple social networks is an important challenge. People often simultaneously appear in multiple social networks that can provide complementary services. However, the knowledge cannot be directly employed due to that they are from different social networks. To solve this problem, we construct an adaptive model to learn user distance in multiple social networks via combining distance metric learning and boosting technologies. To get the solution to our model, we formulate it as a convex optimization problem. Experiments on two real large-scale data sets demonstrate that our method outperforms the compared methods. To the best of our knowledge, the joint learning of metric learning with boosting is first studied in multiple social networks.
Yufei Liu is a CCF student member. He is currently pursuing the Ph.D. degree in software engineering at the College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics. His research interests include data mining, machine learning, and social network analysis. He has presided over a Funding of Jiangsu Innovation Program for Graduate Education, and participated in two National Natural Science Foundation. During his study, he has published multiple research papersincluding CCF conference paper and JCR-Q1 journal paper. He was invited to be a reviewer for IJCNN 2018.