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

[2511.16478] Music Recommendation with Large Language Models: Challenges, Opportunities, and Evaluation Skip to main content Search Submit Donate Log in Computer Science > Information Retrieval arXiv:2511.16478 (cs) [Submitted on 20 Nov 2025 ( v1 ), last revised 25 Aug 2026 (this version, v3)] Title: Music Recommendation with Large Language Models: Challenges, Opportunities, and Evaluation Authors: Elena V. Epure , Yashar Deldjoo , Bruno Sguerra , Markus Schedl , Manuel Moussallam View PDF HTML (experimental) Abstract: Music Recommender Systems (MRSs) 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. 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 (NL) interaction and even allowing models to act as evaluators. This work argues that the shift toward LLM-driven MRSs requires rethinking evaluation. We first review how LLMs have impacted user modeling, item modeling, and NL-based recommendation in music. We then analyze evaluation practices from NLP, highlighting methodologies and open challenges relevant to MRSs. Finally, we synthesize insights, focusing on how LLM prompting applies to MRSs, to outline a structured set of success and risk dimensions. Our goal is to provide the MRSs community with an updated, pedagogical, and cross-disciplinary perspective on evaluation. Comments: Accepted for publication in ACM Transactions on Recommender Systems (TORS) Subjects: Information Retrieval (cs.IR) ; Computation and Language (cs.CL) Cite as: arXiv:2511.16478 [cs.IR] (or arXiv:2511.16478v3 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2511.16478 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Elena V. Epure [ view email ] [v1] Thu, 20 Nov 2025 15:46:27 UTC (729 KB) [v2] Mon, 20 Jul 2026 11:57:11 UTC (799 KB) [v3] Tue, 25 Aug 2026 08:24:13 UTC (725 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.IR < prev | next > new | recent | 2025-11 Change to browse by: cs cs.CL References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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