Metric Learning Combining With Boosting for User Distance Measure in Multiple Social Networks

Abstract

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. Thus, knowledge from different social networks can help overcome the data sparseness problem. However, the knowledge cannot be directly obtained 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. The basic idea of our model is to embed related social networks into a potential feature space, while retaining the topologies of social networks. To get the solution to our model, we formulate it as a convex optimization problem. Moreover, we propose an adaptive user distance measure algorithm whose time complexity is linear with the number of the links. We verify the feasibility and effectiveness of our model on the link prediction 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.