Agents powered by large language models have shown remarkable reasoning and execution capabilities, attracting researchers to explore their potential in the recommendation domain. Previous studies have primarily focused on enhancing the capabilities of either recommendation agents or user agents independently, but have not considered the interaction and collaboration between recommendation agents and user agents. To address this gap, we propose a novel framework named FLOW, which achieves collaboration between the recommendation agent and the user agent by introducing a feedback loop. Specifically, the recommendation agent refines its understanding of the user's preferences by analyzing the user agent's feedback on previously suggested items, while the user agent leverages suggested items to uncover deeper insights into the user's latent interests. This iterative refinement process enhances the reasoning capabilities of both the recommendation agent and the user agent, enabling more precise recommendations and a more accurate simulation of user behavior. To demonstrate the effectiveness of the feedback loop, we evaluate both recommendation performance and user simulation performance on three widely used recommendation domain datasets. The experimental results indicate that the feedback loop can simultaneously improve the performance of both the recommendation and user agents.

翻译：基于大语言模型的智能体已展现出卓越的推理与执行能力，吸引了研究者探索其在推荐领域的潜力。先前的研究主要集中于独立增强推荐智能体或用户智能体的能力，但未考虑推荐智能体与用户智能体之间的交互与协作。为填补这一空白，我们提出了一种名为 FLOW 的新型框架，该框架通过引入反馈循环实现推荐智能体与用户智能体之间的协作。具体而言，推荐智能体通过分析用户智能体对先前推荐项目的反馈，来优化其对用户偏好的理解；而用户智能体则利用推荐项目来更深入地揭示用户的潜在兴趣。这一迭代优化过程增强了推荐智能体与用户智能体双方的推理能力，从而实现更精准的推荐与更真实的用户行为模拟。为验证反馈循环的有效性，我们在三个广泛使用的推荐领域数据集上评估了推荐性能与用户模拟性能。实验结果表明，该反馈循环能够同时提升推荐智能体与用户智能体的性能。