Event-based social networks (EBSNs) have become increasingly popular, which provide online social event management platforms for event organizers to publish and share social events (e.g., outdoor activities). In EBSNs, a major challenge for a social event organizer is how to plan a social event to attract the maximum number of attendance. To organize an event, three essential elements are required, namely, what (i.e., event content), where (i.e., event location), and when (i.e., event time). In this paper, we focus on the social event planning problem, which selects a location and time to hold a social event for the organizer with the given event content, to maximize the total number of participants. The solution of the social event planning problem could support decision-making for social event organizers. For simplicity, we denote a location and time pair as an item in this paper. To solve the social event planning problem, we present a hybrid pairwise Markov random field (H-PMRF) model which takes latent preferences of users, latent attributes of items, similarities between users and similarities between items into consideration. In particular, we construct an undirected graph where each node represents a user's decision on a specific item and each edge represents the relationship between the nodes, define the node potentials and edge potentials which model the dependency relationships between nodes, and give a joint probability distribution over the graph. Further, we adopt the Loopy Belief Propagation algorithm to compute the posterior probability distribution of each node in H-PMRF and select the location and time to hold the event which could attract the maximum number of participants. We collect real-world data set from DoubanEvent website and conduct extensive experiments on it. Experimental results show that the proposed model outperforms several baselines.

中文翻译：

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使用混合成对马尔可夫随机场进行社会事件规划

基于事件的社交网络 (EBSN) 变得越来越流行，它为事件组织者提供在线社交事件管理平台来发布和共享社交事件（例如，户外活动）。在 EBSN 中，社交活动组织者面临的主要挑战是如何策划社交活动以吸引最多的出席人数。要组织一个活动，需要三个基本要素，即什么（即活动内容）、地点（即活动地点）和何时（即事件时间）。在本文中，我们关注社交活动规划问题，即根据给定的活动内容为组织者选择举办社交活动的地点和时间，以最大化参与者的总数。社交活动策划问题的解决方案可以支持社交活动组织者的决策。为简单起见，我们在本文中将位置和时间对表示为一个项目。为了解决社会事件规划问题，我们提出了一种混合成对马尔可夫随机场（H-PMRF）模型，该模型考虑了用户的潜在偏好、项目的潜在属性、用户之间的相似性和项目之间的相似性。特别地，我们构建了一个无向图，其中每个节点代表用户对特定项目的决定，每条边代表节点之间的关系，定义对节点之间的依赖关系建模的节点电位和边电位，并在图上给出联合概率分布。此外，我们采用Loopy Belief Propagation算法计算H-PMRF中每个节点的后验概率分布，并选择可以吸引最大数量参与者的事件的举办地点和时间。我们从豆瓣活动网站收集真实世界的数据集并对其进行广泛的实验。实验结果表明，所提出的模型优于几个基线。我们采用Loopy Belief Propagation算法计算H-PMRF中每个节点的后验概率分布，并选择可以吸引最大数量参与者的事件的举办地点和时间。我们从豆瓣活动网站收集真实世界的数据集并对其进行广泛的实验。实验结果表明，所提出的模型优于几个基线。我们采用Loopy Belief Propagation算法计算H-PMRF中每个节点的后验概率分布，并选择可以吸引最大数量参与者的事件的举办地点和时间。我们从豆瓣活动网站收集真实世界的数据集并对其进行广泛的实验。实验结果表明，所提出的模型优于几个基线。