Group Event Recommendation in Event-Based Social Networks Considering Unexperienced Events

As users in event-based social networks (EBSNs) usually participate in events together with friends, classmates, colleagues, or family members, recommending events to a group of users is attracting increasing attention in EBSNs recent years. However, existing studies are lack of attention to the fact that groups in EBSNs may have potential desires for participating in the unexperienced events. In order to deal with the challenges, including mining implicit friendships between users, simulating the consulting process between users and their friends outside the groups, and simulating the negotiating process among members inside the groups, we propose a t wo- p hase g roup e vent r ecommendation (2PGER) model for EBSNs. First, we leverage information, such as online social behaviors, users’ event participation records, and topological structures of EBSNs to establish a global trust network among users and establish egotrust networks of all users. Then, we perform random walks on the pre-built egotrust network for each user to acquire the user’s predicted preferences on the unexperienced events. Third, we adopt a random walk with restarts (RWR) method to aggregate users’ preferences and recommend top N events to groups. In the end, we compare 2PGER with several baseline approaches on real datasets from Meetup. The results show that 2PGER outperforms baselines.