Abstract
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.
Abstract (translated)
大型语言模型驱动的代理展现出了卓越的推理和执行能力，吸引了研究人员探索其在推荐领域的潜力。先前的研究主要集中在独立提升推荐代理或用户代理的能力上，但没有考虑推荐代理与用户代理之间的交互和协作。为了解决这一差距，我们提出了一种名为FLOW的新框架，通过引入反馈循环实现推荐代理和用户代理间的协作。具体来说，推荐代理通过对之前建议项目的用户代理反馈进行分析来精化对用户偏好的理解；同时，用户代理利用被建议的项目来发现用户潜在兴趣的更深层次见解。这种迭代优化过程增强了推荐代理和用户代理的推理能力，使推荐更加精准，并能更准确地模拟用户行为。为了展示反馈循环的有效性，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和用户模拟性能。实验结果表明，该反馈循环能够同时提升推荐代理和用户代理的表现。
URL
https://arxiv.org/abs/2410.20027
