用大模型工具调用实现智能音乐推荐，自动选择最佳检索方式。
TalkPlay-Tools: Conversational Music Recommendation with LLM Tool Calling
- 大模型规划工具调用顺序，整合布尔过滤、关键词检索等多重方法。
- 在多种推荐场景下表现媲美传统系统，准确率提升显著。
- 适合需要自然对话交互的音乐推荐应用开发人员参考。
尽管大语言模型（LLMs）已成功实现以自然语言交互的生成式推荐，但其推荐行为仍受限，导致元数据或属性过滤等基础组件未被充分利用。我们提出一种基于大模型工具调用的音乐推荐系统，作为统一的检索-重排序管道。该系统将大模型置于端到端推荐流程中，理解用户意图，规划工具调用，并协调专用组件：布尔过滤（SQL）、稀疏检索（BM25）、稠密检索（嵌入相似性）和生成式检索（语义ID）。通过工具规划，系统可预测需使用哪些工具、执行顺序及参数，以匹配用户偏好，在支持多模态的同时无缝集成多种数据库过滤方法。实验表明，该统一工具调用框架在多样推荐场景中表现优异，通过根据用户查询动态选择合适检索策略，为对话式音乐推荐系统开启新范式。
原文摘要 · Abstract (English)
While the recent developments in large language models (LLMs) have successfully enabled generative recommenders with natural language interactions, their recommendation behavior is limited, leaving other simpler yet crucial components such as metadata or attribute filtering underutilized in the system. We propose an LLM-based music recommendation system with tool calling to serve as a unified retrieval-reranking pipeline. Our system positions an LLM as an end-to-end recommendation system that interprets user intent, plans tool invocations, and orchestrates specialized components: boolean filters (SQL), sparse retrieval (BM25), dense retrieval (embedding similarity), and generative retrieval (semantic IDs). Through tool planning, the system predicts which types of tools to use, their execution order, and the arguments needed to find music matching user preferences, supporting diverse modalities while seamlessly integrating multiple database filtering methods. We demonstrate that this unified tool-calling framework achieves competitive performance across diverse recommendation scenarios by selectively employing appropriate retrieval methods based on user queries, envisioning a new paradigm for conversational music recommendation systems.
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