Organized Event Participant Prediction Enhanced by Social Media
  Retweeting Data
Yihong Zhang Takahiro Hara
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.