  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.
