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

arXiv: https://arxiv.org/abs/2405.15114 · HTML: https://mlsanity.com/paper/2405.15114/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2024.

## Built on by (most cited first, 30 of 51)

- [Tool Learning with Large Language Models: A Survey](https://mlsanity.com/paper/2405.17935/) (2024)
- [A Survey of LLM-based Deep Search Agents: Paradigm, Optimization, Evaluation, and Challenges](https://mlsanity.com/paper/2508.05668/) (2025)
- [A Survey on LLM-powered Agents for Recommender Systems](https://mlsanity.com/paper/2502.10050/) (2025)
- [From Allies to Adversaries: Manipulating LLM Tool-Calling through Adversarial Injection](https://mlsanity.com/paper/2412.10198/) (2024)
- [A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information Retrieval](https://mlsanity.com/paper/2503.05659/) (2025)
- [Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models](https://mlsanity.com/paper/2503.16734/) (2025)
- [Unified Parameter-Efficient Unlearning for LLMs](https://mlsanity.com/paper/2412.00383/) (2024)
- [Taming Latency-Memory Trade-Off in MoE-Based LLM Serving via Fine-Grained Expert Offloading](https://mlsanity.com/paper/2502.05370/) (2025)
- [AgentCF++: Memory-enhanced LLM-based Agents for Popularity-aware Cross-domain Recommendations](https://mlsanity.com/paper/2502.13843/) (2025)
- [iAgent: LLM Agent as a Shield between User and Recommender Systems](https://mlsanity.com/paper/2502.14662/) (2025)
- [AgentRecBench: Benchmarking LLM Agent-based Personalized Recommender Systems](https://mlsanity.com/paper/2505.19623/) (2025)
- [MemRec: Collaborative Memory-Augmented Agentic Recommender System](https://mlsanity.com/paper/2601.08816/) (2026)
- [The Future is Agentic: Definitions, Perspectives, and Open Challenges of Multi-Agent Recommender Systems](https://mlsanity.com/paper/2507.02097/) (2025)
- [The Efficiency vs. Accuracy Trade-off: Optimizing RAG-Enhanced LLM Recommender Systems Using Multi-Head Early Exit](https://mlsanity.com/paper/2501.02173/) (2025)
- [Improving LLM-powered Recommendations with Personalized Information](https://mlsanity.com/paper/2502.13845/) (2025)
- [SynWorld: Virtual Scenario Synthesis for Agentic Action Knowledge Refinement](https://mlsanity.com/paper/2504.03561/) (2025)
- [STAR: A Simple Training-free Approach for Recommendations using Large Language Models](https://mlsanity.com/paper/2410.16458/) (2024)
- [CoMaPOI: A Collaborative Multi-Agent Framework for Next POI Prediction Bridging the Gap Between Trajectory and Language](https://mlsanity.com/paper/2505.23837/) (2025)
- [AGENT-CQ: Automatic Generation and Evaluation of Clarifying Questions for Conversational Search with LLMs](https://mlsanity.com/paper/2410.19692/) (2024)
- [Semantic Retrieval Augmented Contrastive Learning for Sequential Recommendation](https://mlsanity.com/paper/2503.04162/) (2025)
- [ScienceDB AI: An LLM-Driven Agentic Recommender System for Large-Scale Scientific Data Sharing Services](https://mlsanity.com/paper/2601.01118/) (2026)
- [ToolBridge: An Open-Source Dataset to Equip LLMs with External Tool Capabilities](https://mlsanity.com/paper/2410.10872/) (2024)
- [TOOL-ED: Enhancing Empathetic Response Generation with the Tool Calling Capability of LLM](https://mlsanity.com/paper/2412.03096/) (2024)
- [LLMs as Orchestrators: Constraint-Compliant Multi-Agent Optimization for Recommendation Systems](https://mlsanity.com/paper/2601.19121/) (2026)
- [Agentic Recommender System with Hierarchical Belief-State Memory](https://mlsanity.com/paper/2605.14401/) (2026)
- [Exploring Recommender System Evaluation: A Multi-Modal User Agent Framework for A/B Testing](https://mlsanity.com/paper/2601.04554/) (2026)
- [SAGER: Self-Evolving User Policy Skills for Recommendation Agent](https://mlsanity.com/paper/2604.14972/) (2026)
- [Autonomous Information Seeking: A Roadmap for Agentic Recommender Systems](https://mlsanity.com/paper/2607.04433/) (2026)
- [Task-Aligned Tool Recommendation for Large Language Models](https://mlsanity.com/paper/2411.09613/) (2024)
- [LLM-Based User Simulation for Low-Knowledge Shilling Attacks on Recommender Systems](https://mlsanity.com/paper/2505.13528/) (2025)

## Abstract (arXiv)

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.