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User engagement prediction plays a critical role for designing interactionstrategies to grow user engagement and increase revenue in online socialplatforms. Through the in-depth analysis of the real-world data from theworld's largest professional social platforms, i.e., LinkedIn, we find thatusers expose diverse engagement patterns, and a major reason for thedifferences in user engagement patterns is that users have different intents.That is, people have different intents when using LinkedIn, e.g., applying forjobs, building connections, or checking notifications, which shows quitedifferent engagement patterns. Meanwhile, user intents and the correspondingengagement patterns may change over time. Although such pattern differences anddynamics are essential for user engagement prediction, differentiating userengagement patterns based on user dynamic intents for better user engagementforecasting has not received enough attention in previous works. In this paper,we proposed a Dynamic Intent Guided Meta Network (DIGMN), which can explicitlymodel user intent varying with time and perform differentiated user engagementforecasting. Specifically, we derive some interpretable basic user intents asprior knowledge from data mining and introduce prior intents in explicitlymodeling dynamic user intent. Furthermore, based on the dynamic user intentrepresentations, we propose a meta predictor to perform differentiated userengagement forecasting. Through a comprehensive evaluation on LinkedInanonymous user data, our method outperforms state-of-the-art baselinessignificantly, i.e., 2.96% and 3.48% absolute error reduction, oncoarse-grained and fine-grained user engagement prediction tasks, respectively,demonstrating the effectiveness of our method.

DIGMN: Dynamic Intent Guided Meta Network for Differentiated User Engagement Forecasting in Online Professional Social Platforms