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arXiv · 2509.19469

MusiCRS: Benchmarking Audio-Centric Conversational Recommendation

Rohan Surana·Amit Namburi·Gagan Mundada·Abhay Lal·Zachary Novack·Julian McAuley·Junda Wu
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Abstract

Conversational recommendation has advanced rapidly with large language models (LLMs), yet music remains a uniquely challenging domain in which effective recommendations require reasoning over audio content beyond what text or metadata can capture. We present MusiCRS, the first benchmark for audio-centric conversational recommendation that links authentic user conversations from Reddit with corresponding tracks. MusiCRS includes 477 high-quality conversations spanning diverse genres (classical, hip-hop, electronic, metal, pop, indie, jazz), with 3,589 unique musical entities and audio grounding via YouTube links. MusiCRS supports evaluation under three input modality configurations: audio-only, query-only, and audio+query, allowing systematic comparison of audio-LLMs, retrieval models, and traditional approaches. Our experiments reveal that current systems struggle with cross-modal integration, with optimal performance frequently occurring in single-modality settings rather than multimodal configurations. This highlights fundamental limitations in cross-modal knowledge integration, as models excel at dialogue semantics but struggle when grounding abstract musical concepts in audio. To facilitate progress, we release the MusiCRS dataset (https://huggingface.co/datasets/rohan2810/MusiCRS), evaluation code (https://github.com/rohan2810/musiCRS), and comprehensive baselines.

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Rohan Surana, Amit Namburi, Gagan Mundada, Abhay Lal, Zachary Novack, Julian McAuley, Junda Wu. 2025-09-23. MusiCRS: Benchmarking Audio-Centric Conversational Recommendation. https://arxiv.org/abs/2509.19469

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