Let Me Do It For You: Towards LLM Empowered Recommendation via Tool Learning

TL;DR

This paper introduces ToolRec, a framework that leverages large language models as surrogate users to improve recommendation accuracy by invoking external tools and capturing nuanced user preferences.

Contribution

It presents a novel LLM-based recommendation framework that uses tool learning to better align recommendations with fine-grained user preferences.

Findings

ToolRec effectively captures user preferences in semantic-rich scenarios.

The framework improves recommendation relevance by integrating external tools.

Experimental results demonstrate significant performance gains over baseline methods.

Abstract

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.