TalkPlay-Tools: Conversational Music Recommendation with LLM Tool
  Calling
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.
68e3a83273e20ab5778421a5
Seungheon Doh
claimed_verified
2025-10-06T12:48:06.326Z
637c3504c292c0fd3f37361f
https://cdn-avatars.huggingface.co/v1/production/uploads/637c3504c292c0fd3f37361f/wyTkbYKi8HufRT65LGN0P.jpeg
seungheon.doh
user
seungheondoh
68e3a83273e20ab5778421a6
Keunwoo Choi
68e3a83273e20ab5778421a7
Juhan Nam
TalkPlay-Tools: Conversational Music Recommendation with LLM Tool
  Calling
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.
A unified LLM-based music recommendation system with tool calling integrates various retrieval methods to enhance user intent interpretation and recommendation performance.
LLMs generative recommenders natural language interactions boolean filters sparse retrieval BM25 dense retrieval embedding similarity generative retrieval semantic IDs tool planning retrieval-reranking pipeline conversational music recommendation systems