In recent years, there has been a growing interest in utilizing reinforcement learning (RL) to optimize long-term rewards in recommender systems. Since industrial recommender systems are typically designed as multi-stage systems, RL methods with a single agent face challenges when optimizing multiple stages simultaneously. The reason is that different stages have different observation spaces, and thus cannot be modeled by a single agent. To address this issue, we propose a novel UNidirectional-EXecution-based multi-agent Reinforcement Learning (UNEX-RL) framework to reinforce the long-term rewards in multi-stage recommender systems. We use the unidirectional execution to model the information flow in multi-stage recommender systems, which is a key difference between multi-stage recommender systems and traditional multi-agent reinforcement learning (MARL) applications. Then, we show that the training of MARL in multi-stage recommender systems faces two challenges, namely the observation dependency and the cascading effect. To tackle these challenges, we provide a cascading information chain (CIC) method to train UNEX-RL effectively. We also discuss practical variance reduction techniques for UNEX-RL. Finally, we show the effectiveness of UNEX-RL on both public datasets and an online recommender system with over 100 million users. Specifically, UNEX-RL reveals a 0.558% gain in users' watch time compared with single-agent RL algorithms in online experiments, highlighting the effectiveness of UNEX-RL in industrial recommender systems.

UNEX-RL: Reinforcing Long-Term Rewards in Multi-Stage Recommender Systems with UNidirectional EXecution

Main Track - Data Mining and Knowledge Management

technical paper

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The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. 

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UNEX-RL: Reinforcing Long-Term Rewards in Multi-Stage Recommender Systems with UNidirectional EXecution

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