  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.
