Followee Recommendation in Event-Based Social Networks

Recent years have witnessed the rapid growth of event-based social networks (EBSNs) such as Plancast and DoubanEvent. In these EBSNs, followee recommendation which recommends new users to follow can bring great benefits to both users and service providers. In this paper, we focus on the problem of followee recommendation in EBSNs. However, the sparsity and imbalance of the social relations in EBSNs make this problem very challenging. Therefore, by exploiting the heterogeneous nature of EBSNs, we propose a new method called Heterogenous Network based Followee Recommendation (HNFR) for our problem. In the HNFR method, to relieve the problem of data sparsity, we combine the explicit and latent features captured from both the online social network and the offline event participation network of an EBSN. Moreover, to overcome the problem of data imbalance, we propose a Bayesian optimization framework which adopts pairwise user preference on both the social relations and the events, and aims to optimize the area under ROC curve (AUC). The experiments on real-world data demonstrate the effectiveness of our method.