Organized Event Participant Prediction Enhanced by Social Media Retweeting Data
This work addresses data insufficiency in event participant prediction for web platforms, offering a domain-specific enhancement that is incremental in nature.
The paper tackles the problem of predicting potential participants for organized events by leveraging social media retweeting data to overcome data sparsity, achieving consistent performance improvements over baselines, especially in warm test cases and when target domain data is limited.
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.