KEGNN: Knowledge-Enhanced Graph Neural Networks for User Engagement Prediction

Accurate user engagement prediction is critical to the success of crowdsourcing in online communities. This study focuses on analyzing user behavior in crowd-driven platforms, addressing the challenge of predicting engagement levels based on behavioral dynamics in online knowledge-sharing environments. We propose a comprehensive framework, Knowledge-Enhanced Graph Neural Networks (KEGNN), which begins by collecting and refining user activity data from a real-world platform and quantifying user engagement using the RFE (Recency, Frequency, Engagement) model to extract meaningful insights from the comprehensive record of the user's behavior. KEGNN leverages a graph neural network (GNN)-based architecture to capture temporal and spatial patterns in user dynamics, incorporating user-generated textual knowledge to enhance prediction performance. Extensive experiments on data from Just One Giant Lab (JOGL), an open knowledge-sharing platform, demonstrate that KEGNN outperforms adapted baseline models originally designed for other domains. The results highlight its effectiveness in predicting short- and long-term user engagement. Our framework provides a robust foundation for analyzing online user behavior and holds significant potential for adaptation to similar challenges across various domains. The source code is publicly available at: https://github.com/charliefanfan/ICMR-GNN.