Event recommendation in social networks based on reverse random walk and participant scale control

With the merging of cyber world and physical world, event-based social networks have been playing an important role in promoting the spread of offline social events through online channels. Event recommendation in social networks, which is to recommend a list of upcoming events to a user according to his preference, has attracted a lot of research interests recently. In this paper, we study the event recommendation problem based on the graph theory. We first construct a heterogeneous graph to represent the interactions among different types of entities in an event-based social network. Based on the constructed graph, we propose a novel event scoring algorithm called reverse random walk with restart to obtain the user-event recommendation matrix. In practice, the participant capacity of an event may be constrained to a limited number of users. Then based on the user-event recommendation matrix, we further propose two participant scale control algorithms to coordinate unbalanced user arrangements among events. After the rearrangement, each user will be assigned a list of recommended events, which considers both local user preference and global event capacity. Experiment results on Meetup dataset show that the proposed method outperforms the state-of-art algorithms in terms of higher recommendation precision and larger recommendation coverage.