While the recommendation system (RS) has advanced significantly through deep learning, current RS approaches usually train and fine-tune models on task-specific datasets, limiting their generalizability to new recommendation tasks and their ability to leverage external knowledge due to model scale and data size constraints. Thus, we designed an LLM-powered autonomous recommender agent, RecMind, which is capable of leveraging external knowledge, utilizing tools with careful planning to provide zero-shot personalized recommendations. We propose a Self-Inspiring algorithm to improve the planning ability. At each intermediate step, the LLM self-inspires to consider all previously explored states to plan for the next step. This mechanism greatly improves the model's ability to comprehend and utilize historical information in planning for recommendation. We evaluate RecMind's performance in various recommendation scenarios. Our experiment shows that RecMind outperforms existing zero/few-shot LLM-based recommendation baseline methods in various tasks and achieves comparable performance to a fully trained recommendation model P5.

翻译：尽管推荐系统（RS）通过深度学习取得了显著进展，但当前的RS方法通常在特定任务数据集上训练和微调模型，受限于模型规模与数据量，难以泛化至新推荐任务或利用外部知识。为此，我们设计了一种由大语言模型驱动的自主推荐智能体RecMind，它能够借助外部知识并基于精细规划使用工具，实现零样本个性化推荐。我们提出自启发（Self-Inspiring）算法以提升规划能力：在每步中间过程中，LLM通过自我激发回顾所有先前探索的状态，从而规划下一步行动。该机制显著增强了模型在推荐规划中理解与运用历史信息的能力。我们在多种推荐场景下评估RecMind的性能。实验表明，RecMind在各项任务中均优于现有的零/少样本LLM推荐基线方法，其表现可与完全训练的推荐模型P5相媲美。