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，受限于模型规模和数据规模，难以泛化至新的推荐任务并充分利用外部知识。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，其具备利用外部知识、通过精细规划调用工具的能力，可实现零样本个性化推荐。我们提出了一种自激励算法以提升规划能力：在每一步中间过程，大语言模型通过自我激励来考虑所有历史探索状态，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利用历史信息的能力。我们在多种推荐场景下评估了RecMind的性能。实验表明，RecMind在各类任务中均优于现有基于大语言模型的零/少样本推荐基线方法，并达到了与完全训练的推荐模型P5相当的性能。