ProRL: Effective Reinforcement Learning for Proactive Recommendation via Rectified Policy Gradient Estimation 事件

ProRL: Effective Reinforcement Learning for Proactive Recommendation via Rectified Policy Gradient Estimation arXiv:2605.28293v1 Announce Type: cross Abstract: Proactive Recommender Systems (PRSs) aim to guide user preference shift toward target items by generating paths of intermediate recommendations. Reinforcement learning (RL) provides a principled framework for optimizing such sequential decision tasks, as path rewards can naturally capture both short-term acceptance and long-term guidance