Stratified Expert Cloning with Adaptive Selection for User Retention in Large-Scale Recommender Systems

User retention has emerged as a critical challenge in large-scale recommender systems, significantly impacting the long-term success of online platforms. Existing methods often focus on short-term engagement metrics, failing to capture the complex dynamics of user preferences and behaviors over extended periods. While reinforcement learning (RL) approaches have shown promise in optimizing long-term rewards, they face difficulties in credit assignment, sample efficiency, and exploration when applied to the user retention problem. In this work, we propose Stratified Expert Cloning (SEC), a novel imitation learning framework that effectively leverages abundant logged data from high-retention users to learn robust recommendation policies. SEC introduces three key innovations: 1) a multi-level expert stratification strategy that captures the nuances in expert user behaviors at different retention levels; 2) an adaptive expert selection mechanism that dynamically assigns users to the most suitable policy based on their current state and historical retention level; and 3) an action entropy regularization technique that promotes recommendation diversity and mitigates the risk of policy collapse. Through extensive offline experiments and online A/B tests on two major video platforms, Kuaishou and Kuaishou Lite, with hundreds of millions of daily active users, we demonstrate SEC's significant improvements over state-of-the-art methods in user retention. The results demonstrate significant improvements in user retention, with cumulative lifts of 0.098\% and 0.122\% in active days on Kuaishou and Kuaishou Lite respectively, additionally bringing tens of thousands of daily active users to each platform.