TalkPlay-Tools：基于大语言模型工具调用的对话式音乐推荐系统

摘要

尽管大型语言模型（LLMs）的最新进展已成功实现了具备自然语言交互能力的生成式推荐系统，但其推荐行为仍存在局限，导致系统中其他更为基础却至关重要的组件，如元数据或属性过滤，未能得到充分利用。我们提出了一种基于LLM的音乐推荐系统，该系统通过工具调用构建统一的检索-重排序流程。我们的系统将LLM定位为端到端的推荐系统，能够解析用户意图、规划工具调用，并协调多个专门组件：布尔过滤器（SQL）、稀疏检索（BM25）、密集检索（嵌入相似度）以及生成式检索（语义ID）。通过工具规划，系统预测应使用哪些类型的工具、它们的执行顺序及所需参数，以寻找符合用户偏好的音乐，支持多样化的交互模式，同时无缝整合多种数据库过滤方法。我们证明，这一统一的工具调用框架通过根据用户查询选择性地采用适当的检索方法，在多种推荐场景中均展现出竞争力，为对话式音乐推荐系统开辟了新的范式。

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.

TalkPlay-Tools：基于大语言模型工具调用的对话式音乐推荐系统

TalkPlay-Tools: Conversational Music Recommendation with LLM Tool Calling

摘要

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