Music Recommendation with Large Language Models: Challenges, Opportunities, and Evaluation

• URL: http://arxiv.org/abs/2511.16478v1
• Date: Thu, 20 Nov 2025 15:46:27 GMT
• Title: Music Recommendation with Large Language Models: Challenges, Opportunities, and Evaluation
• Authors: Elena V. Epure, Yashar Deldjoo, Bruno Sguerra, Markus Schedl, Manuel Moussallam,
• Abstract summary: Music Recommender Systems (MRS) have long relied on an information-retrieval framing.<n>The emergence of Large Language Models (LLMs) disrupts this framework.<n>LLMs are generative rather than ranking-based, making standard accuracy metrics questionable.
• Score: 14.210401534321806
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Music Recommender Systems (MRS) have long relied on an information-retrieval framing, where progress is measured mainly through accuracy on retrieval-oriented subtasks. While effective, this reductionist paradigm struggles to address the deeper question of what makes a good recommendation, and attempts to broaden evaluation, through user studies or fairness analyses, have had limited impact. The emergence of Large Language Models (LLMs) disrupts this framework: LLMs are generative rather than ranking-based, making standard accuracy metrics questionable. They also introduce challenges such as hallucinations, knowledge cutoffs, non-determinism, and opaque training data, rendering traditional train/test protocols difficult to interpret. At the same time, LLMs create new opportunities, enabling natural-language interaction and even allowing models to act as evaluators. This work argues that the shift toward LLM-driven MRS requires rethinking evaluation. We first review how LLMs reshape user modeling, item modeling, and natural-language recommendation in music. We then examine evaluation practices from NLP, highlighting methodologies and open challenges relevant to MRS. Finally, we synthesize insights-focusing on how LLM prompting applies to MRS, to outline a structured set of success and risk dimensions. Our goal is to provide the MRS community with an updated, pedagogical, and cross-disciplinary perspective on evaluation.

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