Details of Research Outputs

With the popularity of mobile computing and social media, various kinds of online event-based social network (EBSN) platforms, such as Meetup, Plancast and Whova, is gaining in prominence. A fundamental task of managing EBSN platforms is to recommend suitable social events to potential users according to the following three factors: distances between events and users, attribute similarities between events and users and friend relationships among users. However, none of existing approaches consider all aforementioned influential factors when they recommend users to proper events. Furthermore, existing recommendation strategies neglect the bottleneck cases on the global recommendation. Thus, it is impossible for the existing recommendation solutions to achieve the optimal utility in real-world scenarios. In this paper, we first formally define the problem of bottleneck-Aware social event arrangement (BSEA), which is proven to be NP-hard. To solve the BSEA problem approximately, we devise two greedy-based heuristic algorithms, Greedy and Random+Greedy. In particular, the Random+Greedy algorithm is faster and more effective than the Greedy algorithm in most cases. Finally, we conduct extensive experiments on real and synthetic datasets which verify the efficiency and accuracy of our proposed algorithms.