Nowadays, many platforms on the Web offer organized events, 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 model. In this paper, we propose to utilize social media retweeting activity data 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 utilizes 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, especially with the warm test cases, and when target domain data is limited.

翻译：当前，网络上的许多平台提供有组织事件，允许用户担任组织者或参与者。对于此类平台而言，预测潜在事件参与者具有重要意义。现有研究倾向于借鉴推荐技术来解决该问题。然而，与电商商品及其购买行为相比，事件及其参与行为通常发生频率极低，导致数据量可能不足以训练出精确模型。本文提出利用社交媒体转发活动数据来增强事件参与者预测模型的学习效果。我们构建了一个联合知识图谱，将社交媒体与目标领域相衔接，其前提假设是事件描述与推文使用同一种语言。此外，我们提出了一种学习模型，能够更有效地利用转发信息进行目标领域预测。我们在两个真实数据场景中进行了全面实验，每个场景均设置了不同规模的训练数据，并包含热启动与冷启动测试用例。评估结果表明，我们的方法始终优于多个基线模型，尤其在热启动测试用例及目标领域数据有限的条件下，性能提升更为显著。