Event-based social networks (EBSNs) connect online and offline lives. They allow online users with similar interests to get together in real life. Attendance prediction for activities in EBSNs has attracted a lot of attention and several factors have been studied. However, the prediction accuracy is not very good for some special activities, such as outdoor activities. Moreover, a very important factor, the weather, has not been well exploited. In this work, we strive to understand how the weather factor impacts activity attendance, and we explore it to improve attendance prediction from the organizer’s view. First, we classify activities into two categories: the outdoor and the indoor activities. We study the different ways that weather factors may impact these two kinds of activities. We also introduce a new factor of event duration. By integrating the above factors with user interest and user-event distance, we build a model of attendance prediction with the weather named GBT-W , based on the Gradient Boosting Tree. Furthermore, we develop a platform to help event organizers estimate the possible number of activity attendance with different settings (e.g., different weather, location) to effectively plan their events. We conduct extensive experiments, and the results show that our method has a better prediction performance on both the outdoor and the indoor activities, which validates the reasonability of considering weather and duration.

中文翻译：

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探索天气数据以预测基于事件的社交网络中的活动出勤率

基于事件的社交网络 (EBSN) 连接线上和线下生活。它们允许具有相似兴趣的在线用户在现实生活中聚在一起。EBSN 中活动的出勤率预测引起了很多关注，并且已经研究了几个因素。但是，对于一些特殊的活动，例如户外活动，预测精度不是很好。此外，一个非常重要的因素，即天气，并没有得到很好的利用。在这项工作中，我们努力了解天气因素如何影响活动出勤率，并从组织者的角度对其进行探索以改进出勤率预测。首先，我们将活动分为两类：户外活动和室内活动。我们研究了天气因素可能影响这两种活动的不同方式。我们还引入了一个新的事件持续时间因素。GBT-W，基于梯度提升树。此外，我们开发了一个平台来帮助活动组织者估计在不同设置（例如，不同天气、地点）下可能参加的活动数量，以有效地计划他们的活动。我们进行了大量的实验，结果表明我们的方法对室外和室内活动都有更好的预测性能，这验证了考虑天气和持续时间的合理性。