A Bilateral Recommendation Strategy for Mobile Event-Based Social Networks

Mobile Event-Based Social Network (EBSN) platforms, such as Meetup and Plancast, have become increasingly popular for online organization of offline (in-person) events. The problem of the existing techniques in ESBN is that they do not reflect the bilateral (two-way) nature of efficient event planning: 1) events enroll more influential participants, and 2) participants are arranged to events they are most interested in. In this paper, we address this weakness by formally defining the bilateral recommendation problem and making two contributions to solving this problem: (a) by analyzing all types of the user's behaviors during the selection session, we can accurately predict which event the users will eventually choose to participate in, and (b) by introducing the concepts of interpersonal similarity and interaction strength in EBSNs, we can calculate the interactive influence of users. We report the results of extensive experiments on real datasets that confirm the improved precision, effectiveness and scalability of our proposed bilateral recommendation strategy as compared to the state of the art.