Conventional recommender systems (RSs) face challenges in precisely capturing users' fine-grained preferences. Large language models (LLMs) have shown capabilities in commonsense reasoning and leveraging external tools that may help address these challenges. However, existing LLM-based RSs suffer from hallucinations, misalignment between the semantic space of items and the behavior space of users, or overly simplistic control strategies (e.g., whether to rank or directly present existing results). To bridge these gap, we introduce ToolRec, a framework for LLM-empowered recommendations via tool learning that uses LLMs as surrogate users, thereby guiding the recommendation process and invoking external tools to generate a recommendation list that aligns closely with users' nuanced preferences. We formulate the recommendation process as a process aimed at exploring user interests in attribute granularity. The process factors in the nuances of the context and user preferences. The LLM then invokes external tools based on a user's attribute instructions and probes different segments of the item pool. We consider two types of attribute-oriented tools: rank tools and retrieval tools. Through the integration of LLMs, ToolRec enables conventional recommender systems to become external tools with a natural language interface. Extensive experiments verify the effectiveness of ToolRec, particularly in scenarios that are rich in semantic content.

翻译：传统推荐系统在精确捕捉用户细粒度偏好方面面临挑战。大语言模型在常识推理和利用外部工具方面展现出潜力，可能有助于解决这些挑战。然而，现有基于大语言模型的推荐系统存在幻觉问题、物品语义空间与用户行为空间之间的错位，或过于简化的控制策略（例如仅选择排序或直接呈现现有结果）。为弥补这些不足，我们提出了ToolRec框架——一种通过工具学习实现大语言模型赋能的推荐系统，该框架将大语言模型作为用户代理，从而指导推荐过程并调用外部工具生成与用户细微偏好高度契合的推荐列表。我们将推荐过程形式化为在属性粒度上探索用户兴趣的过程。该过程综合考虑上下文情境与用户偏好的细微差异。大语言模型根据用户的属性指令调用外部工具，并对物品池的不同分区进行探测。我们设计了两种面向属性的工具类型：排序工具与检索工具。通过大语言模型的整合，ToolRec使传统推荐系统能够成为具有自然语言接口的外部工具。大量实验验证了ToolRec的有效性，尤其在语义内容丰富的场景中表现突出。