A Novel Random Walk and Scale Control Method for Event Recommendation

Recently, social event recommendation, which is to recommend a list of upcoming events to a user, has attracted a lot of research interests. In this paper, we first construct a heterogeneous graph to express the interactions among different types of entities in event-based social network. Based on the constructed graph, we propose a novel recommendation algorithm called reverse random walk with restart to obtain the recommended events for each user. Since the participant capacity of an event may be constrained to a limited number of users, we further propose a scale control algorithm to coordinate unbalanced user arrangements among events. Experiment results on Meetup dataset show that the proposed method outperforms the state-of-art algorithms in terms of higher recommendation precision, larger recommendation coverage.