Supervised Learning-enhanced Multi-Group Actor Critic for Live Stream Allocation in Feed

Reinforcement Learning (RL) has been widely applied in recommendation systems to capture long-term user engagement, thus improving dwelling time and improving user retention. In the context of a short video & live stream mixed recommendation scenario, the live stream recommendation system (RS) decides whether to inject at most one live stream into the video feed for each user request. To maximize long-term user engagement, it is crucial to determine an optimal live stream injection policy for accurate live stream allocation. However, traditional RL algorithms often face divergence and instability problems, and these issues may cause too many live stream allocations, which interrupts the user's short-video interest and leads to a decrease in the user's app usage duration. To address these challenges, we propose a novel Supervised Learning-enhanced Multi-Group Actor Critic algorithm (SL-MGAC). Specifically, we introduce a supervised learning-enhanced actor critic framework that incorporates variance reduction techniques, where multi-task reward learning helps restrict bootstrapping error accumulation during critic learning. Additionally, we design a multi-group state decomposition module for both actor and critic networks to reduce prediction variance and improve model stability. We also propose a novel reward function to prevent overly greedy live-stream allocation. Empirically, we evaluate the SL-MGAC algorithm using offline policy evaluation (OPE) and online A/B testing. Experimental results demonstrate that the proposed method not only outperforms baseline methods but also exhibits enhanced stability in online recommendation scenarios.