Who should you follow? Combining learning to rank with social influence for informative friend recommendation

Social network sites have gradually taken the place of traditional media for people to receive the latest information. To receive novel information, users of social network sites are encouraged to establish social relations. The updates shared by friends form social update streams that provide people with up-to-date information. However, having too many friends can lead to an information overload problem causing users to be overwhelmed by the huge number of updates shared continuously by numerous friends. This information overload problem may affect user intentions to join social network sites and thereby possibly reduce the sites' advertising earnings, which are based on the number of users. In this paper, we propose a learning-based recommendation method which suggests informative friends to users, where an informative friend is a friend whose posted updates are liked by the user. Techniques of learning to rank are designed to analyze user behavior and to model the latent preferences of users and updates. At the same time, the learning model is incorporated with social influence to enhance the learned preferences. Informative friends are recommended if the preferences of the updates that they share are highly associated with the preferences of a target user.