  With the rapid growth and prevalence of social network applications (Apps) in
recent years, understanding user engagement has become increasingly important,
to provide useful insights for future App design and development. While several
promising neural modeling approaches were recently pioneered for accurate user
engagement prediction, their black-box designs are unfortunately limited in
model explainability. In this paper, we study a novel problem of explainable
user engagement prediction for social network Apps. First, we propose a
flexible definition of user engagement for various business scenarios, based on
future metric expectations. Next, we design an end-to-end neural framework,
FATE, which incorporates three key factors that we identify to influence user
engagement, namely friendships, user actions, and temporal dynamics to achieve
explainable engagement predictions. FATE is based on a tensor-based graph
neural network (GNN), LSTM and a mixture attention mechanism, which allows for
(a) predictive explanations based on learned weights across different feature
categories, (b) reduced network complexity, and (c) improved performance in
both prediction accuracy and training/inference time. We conduct extensive
experiments on two large-scale datasets from Snapchat, where FATE outperforms
state-of-the-art approaches by ${\approx}10\%$ error and ${\approx}20\%$
runtime reduction. We also evaluate explanations from FATE, showing strong
quantitative and qualitative performance.
