Platform-Oriented Event Time Allocation

Online Event-based social networks (EBSNs), such as Meetup and Whova, which provide platforms for users to publish, arrange and participate in events, have become increasingly popular. A major challenge for managing EBSNs is to generate the most satisfactory event arrangement, i.e., events are scheduled at the reasonable time to attract maximum number of participants. Existing approaches usually focus on assigning a set of events organized to time intervals, but ignore the competitive relationships among different event organizers, which will lead to event time allocations unacceptable to organizers. Thus, a more intelligent EBSNs platform that allocates social events properly in a global view (i.e., the perspective of platform) is desired. In this paper, we first formally define the problem of Platform-oriented Event Time Allocation (PETA), which contains two parts: the prediction of event feasible time period and the event time allocation. Unfortunately, we find that the PETA problem is NP-hard due to the global conflict constraints on events. Thus, we propose a method to calculate event feasible time period based on event time prediction, and then design a greedy algorithm and two approximation algorithms to solve the PETA problem. Finally, we conduct extensive experiments on both real and synthetic datasets to test the effectiveness and efficiency of the proposed algorithms.

Automated summary

Online Event-based social networks (EBSNs) like Meetup and Whova are popular platforms where users can publish, arrange, and participate in events. Managing EBSNs faces the challenge of generating satisfactory event arrangements that attract the maximum number of participants. Existing approaches overlook the competitive relationships among event organizers, leading to unacceptable event time allocations. Thus, this paper proposes an intelligent EBSNs platform that allocates social events properly in a global view. The platform-oriented Event Time Allocation (PETA) problem is defined, and a method, along with greedy and approximation algorithms, is proposed to solve it. Extensive experiments on real and synthetic datasets demonstrate the effectiveness and efficiency of the proposed algorithms.