Organized event is an important form of human activity. Nowadays, many digital platforms offer organized events on the Internet, allowing users to be organizers or participants. For such platforms, it is beneficial to predict potential event participants. Existing work on this problem tends to borrow recommendation techniques. However, compared to e-commerce items and purchases, events and participation are usually of a much smaller frequency, and the data may be insufficient to learn an accurate prediction model. In this paper, we propose to utilize social media retweeting activity to enhance the learning of event participant prediction models. We create a joint knowledge graph to bridge the social media and the target domain, assuming that event descriptions and tweets are written in the same language. Furthermore, we propose a learning model that utilize retweeting information for the target domain prediction more effectively. We conduct comprehensive experiments in two scenarios with real-world data. In each scenario, we set up training data of different sizes, as well as warm and cold test cases. The evaluation results show that our approach consistently outperforms several baseline models in both warm and cold tests.

有组织的活动是人类活动的重要形式。如今，许多数字平台在互联网上提供有组织的活动，允许用户成为组织者或参与者。对于此类平台，预测潜在的事件参与者是有益的。关于这个问题的现有工作倾向于借用推荐技术。然而，与电商商品和购买相比，事件和参与的频率通常要小得多，并且数据可能不足以学习准确的预测模型。在本文中，我们建议利用社交媒体转发活动来增强事件参与者预测模型的学习。我们创建一个联合知识图来连接社交媒体和目标领域，假设事件描述和推文是用相同的语言编写的。此外，我们提出了一种学习模型，可以更有效地利用转发信息进行目标域预测。我们使用真实世界数据在两个场景中进行了全面的实验。在每个场景中，我们设置不同大小的训练数据，以及冷热测试用例。评估结果表明，我们的方法在热测试和冷测试中始终优于几种基线模型。