In recent years, online event-based social networks (EBSNs), such as Meetup and Whova, which offer platforms for users to plan, publish and arrange events, have gained popularity and rapid development. A fundamental task of managing EBSNs is to recommend a personalized and suitable sequence of events for potential users. However, none of the existing approaches consider both personalized recommendation and event arrangement at the same time. In addition, as to event-to-user recommendation, there are many existing methods, but they neglect individual differences between users and the differences between event sets that users can participate in. As to event arrangement, the different requirements of service providers are ignored, such as time efficiency, user experience, etc. In order to address these problems, in this paper, we first define the problem of Personalized Event–Participant Arrangement based on user interests (PEPA), which contains two parts: personalized recommendation and event–participant arrangement. For the personalized recommendation, we propose a personalized recommendation framework, which treats each user differently and computes the interest value toward events for each user, and we put forward a concept of user curiosity for the first time to overcome the problem of neglecting the differences between event sets for each user. For the event arrangement, we devise a Pruning-based greedy algorithm and two improvement strategies called Conflict-set strategy and Relaxation strategy to improve the runtime efficiency and the user experience respectively. Finally, we conduct extensive experiments on real and synthetic datasets to verify the efficiency and the user satisfaction of the proposed algorithms.

近年来，Meetup和Whova等基于事件的在线社交网络（EBSN）为用户提供了计划，发布和安排事件的平台，并已得到普及和快速发展。管理EBSN的基本任务是为潜在用户推荐个性化且合适的事件序列。但是，现有方法都没有同时考虑个性化推荐和事件安排。另外，关于事件对用户的推荐，有很多现有的方法，但是它们忽略了用户之间的个体差异以及用户可以参与的事件集之间的差异。对于事件安排，服务提供商的不同要求被忽略了。 ，例如时间效率，用户体验等。为了解决这些问题，在本文中，我们首先根据用户兴趣（PEPA）定义个性化事件与参与者安排的问题，该问题包含两个部分：个性化推荐和事件与参与者安排。对于个性化推荐，我们提出了个性化推荐框架，该框架对每个用户进行不同对待并计算每个用户对事件的兴趣价值，并且首次提出了用户好奇心的概念，以克服忽视两者之间差异的问题。每个用户的事件集。对于事件安排，我们设计了一种基于修剪的贪婪算法和两种改进策略，分别称为冲突集策略和放松策略，以分别提高运行时效率和用户体验。最后，