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