Stratified Expert Cloning for Retention-Aware Recommendation at Scale

User retention is critical in large-scale recommender systems, significantly influencing online platforms' long-term success. Existing methods typically focus on short-term engagement, neglecting the evolving dynamics of user behaviors over time. Reinforcement learning (RL) methods, though promising for optimizing long-term rewards, face challenges like delayed credit assignment and sample inefficiency. We introduce Stratified Expert Cloning (SEC), an imitation learning framework that leverages abundant interaction data from high-retention users to learn robust policies. SEC incorporates: 1) multi-level expert stratification to model diverse retention behaviors; 2) adaptive expert selection to dynamically match users with appropriate policies based on their state and retention history; and 3) action entropy regularization to enhance recommendation diversity and policy generalization. Extensive offline evaluations and online A/B tests on major video platforms (Kuaishou and Kuaishou Lite) with hundreds of millions of users validate SEC's effectiveness. Results show substantial improvements, achieving cumulative lifts of 0.098% and 0.122% in active days on the two platforms respectively, each translating into over 200,000 additional daily active users.