Recent advancements in instructing Large Language Models (LLMs) to utilize
external tools and execute multi-step plans have significantly enhanced their
ability to solve intricate tasks, ranging from mathematical problems to
creative writing. Yet, there remains a notable gap in studying the capacity of
LLMs in responding to personalized queries such as a recommendation request. To
bridge this gap, we have designed an LLM-powered autonomous recommender agent,
RecMind, which is capable of providing precise personalized recommendations
through careful planning, utilizing tools for obtaining external knowledge, and
leveraging individual data. We propose a novel algorithm, Self-Inspiring, to
improve the planning ability of the LLM agent. At each intermediate planning
step, the LLM 'self-inspires' to consider all previously explored states to
plan for next step. This mechanism greatly improves the model's ability to
comprehend and utilize historical planning information for recommendation. We
evaluate RecMind's performance in various recommendation scenarios, including
rating prediction, sequential recommendation, direct recommendation,
explanation generation, and review summarization. Our experiment shows that
RecMind outperforms existing zero/few-shot LLM-based recommendation methods in
different recommendation tasks and achieves competitive performance to a recent
model P5, which requires fully pre-train for the recommendation tasks.

使用 Large Language Models (LLMs) 进行个性化推荐任务的研究，设计了一种基于 LLM 的自主推荐代理系统 RecMind，通过精心规划、利用外部知识工具和个人数据，以及提出的 Self-Inspiring 算法来改善规划能力，实现了准确的个性化推荐。实验证明 RecMind 在多种推荐场景下表现优异，超过了现有的零 / 少样本 LLM-based 推荐方法，并与最近的预训练模型 P5 达到了竞争性的性能。