用大模型增强强化学习，让推荐系统更懂用户长期兴趣。
LLM-Enhanced Reinforcement Learning for Long-Term User Satisfaction in Interactive Recommendation
- 大模型负责选内容方向，强化学习精调具体推荐
- 在真实数据集上长期满意度提升显著优于现有方法
- 适合需要持续优化用户体验的推荐场景
交互式推荐系统能根据用户反馈动态调整，但常因过度关注短期偏好导致内容同质化和信息茧房。现有方法多在静态或单次设定下工作，忽视用户兴趣的长期演变。强化学习可建模序列决策以优化长期满意度，但受限于稀疏、长尾的用户-物品交互及有限的语义规划能力。本文提出 LERL：一种融合大模型语义规划与强化学习精细适应的分层推荐框架。高层由大模型选择语义多样内容类别，底层强化学习在选定语义空间内个性化推荐物品。该设计缩小动作空间，提升规划效率，减少冗余内容曝光。在真实数据集上的大量实验表明，相比前沿基线，LERL显著提升长期用户满意度。代码已开源。
原文摘要 · Abstract (English)
Interactive recommender systems can dynamically adapt to user feedback, but often suffer from content homogeneity and filter bubble effects due to overfitting short-term user preferences. While recent efforts aim to improve content diversity, they predominantly operate in static or one-shot settings, neglecting the long-term evolution of user interests. Reinforcement learning provides a principled framework for optimizing long-term user satisfaction by modeling sequential decision-making processes. However, its application in recommendation is hindered by sparse, long-tailed user-item interactions and limited semantic planning capabilities. In this work, we propose LLM-Enhanced Reinforcement Learning (LERL), a novel hierarchical recommendation framework that integrates the semantic planning power of LLM with the fine-grained adaptability of RL. LERL consists of a high-level LLM-based planner that selects semantically diverse content categories, and a low-level RL policy that recommends personalized items within the selected semantic space. This hierarchical design narrows the action space, enhances planning efficiency, and mitigates overexposure to redundant content. Extensive experiments on real-world datasets demonstrate that LERL significantly improves long-term user satisfaction when compared with state-of-the-art baselines. The implementation of LERL is available at https://github.com/1163710212/LERL.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。