Research On Social Event Recommendation Based On Heterogenous Graph Model

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.