The rapid evolution of the web has led to an exponential growth in content. Recommender systems play a crucial role in Human-Computer Interaction (HCI) by tailoring content based on individual preferences. Despite their importance, challenges persist in balancing recommendation accuracy with user satisfaction, addressing biases while preserving user privacy, and solving cold-start problems in cross-domain situations. This research argues that addressing these issues is not solely the recommender systems' responsibility, and a human-centered approach is vital. We introduce the RAH Recommender system, Assistant, and Human) framework, an innovative solution with LLM-based agents such as Perceive, Learn, Act, Critic, and Reflect, emphasizing the alignment with user personalities. The framework utilizes the Learn-Act-Critic loop and a reflection mechanism for improving user alignment. Using the real-world data, our experiments demonstrate the RAH framework's efficacy in various recommendation domains, from reducing human burden to mitigating biases and enhancing user control. Notably, our contributions provide a human-centered recommendation framework that partners effectively with various recommendation models.

翻译：网络的快速发展导致了内容的指数级增长。推荐系统通过根据个体偏好定制内容，在人机交互中扮演着关键角色。尽管其重要性不言而喻，但在平衡推荐精度与用户满意度、解决偏差同时保护用户隐私，以及应对跨领域冷启动问题等方面仍面临挑战。本研究认为，解决这些问题并非仅是推荐系统的责任，采取以人为中心的方法至关重要。我们提出RAH（推荐系统、助手与人类）框架，这是一种创新性解决方案，融合了基于LLM的智能体（包括感知、学习、行动、评判与反思），强调与用户个性的对齐。该框架利用“学习-行动-评判”循环及反思机制来提升用户对齐效果。通过真实世界数据的实验，我们在多个推荐领域验证了RAH框架的有效性——从减轻人类负担到缓解偏差，再到增强用户控制力。值得注意的是，我们的贡献在于提供了一个能够与各类推荐模型有效协作的、以人为中心的推荐框架。