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[2602.12301] Beyond Musical Descriptors: Extracting Preference-Bearing Intent in Music Queries
Summary
This article presents a novel dataset, MusicRecoIntent, aimed at understanding user intent in music queries by analyzing descriptors and their roles in user preferences.
Why It Matters
Understanding user intent in music queries is crucial for improving music recommendation systems. This research introduces a new dataset and benchmarks for evaluating how well large language models can interpret nuanced user preferences, thereby enhancing the effectiveness of music understanding technologies.
Key Takeaways
- Introduces MusicRecoIntent, a dataset of 2,291 annotated music requests.
- Focuses on extracting preference-bearing intent from user queries.
- Demonstrates that large language models can capture explicit descriptors but struggle with context-dependent meanings.
- Provides a benchmark for improving LLM-based music understanding systems.
- Highlights the importance of user intent in enhancing music recommendation algorithms.
Computer Science > Sound arXiv:2602.12301 (cs) [Submitted on 11 Feb 2026] Title:Beyond Musical Descriptors: Extracting Preference-Bearing Intent in Music Queries Authors:Marion Baranes, Romain Hennequin, Elena V. Epure View a PDF of the paper titled Beyond Musical Descriptors: Extracting Preference-Bearing Intent in Music Queries, by Marion Baranes and 2 other authors View PDF HTML (experimental) Abstract:Although annotated music descriptor datasets for user queries are increasingly common, few consider the user's intent behind these descriptors, which is essential for effectively meeting their needs. We introduce MusicRecoIntent, a manually annotated corpus of 2,291 Reddit music requests, labeling musical descriptors across seven categories with positive, negative, or referential preference-bearing roles. We then investigate how reliably large language models (LLMs) can extract these music descriptors, finding that they do capture explicit descriptors but struggle with context-dependent ones. This work can further serve as a benchmark for fine-grained modeling of user intent and for gaining insights into improving LLM-based music understanding systems. Comments: Subjects: Sound (cs.SD); Computation and Language (cs.CL); Information Retrieval (cs.IR); Machine Learning (cs.LG); Audio and Speech Processing (eess.AS) Cite as: arXiv:2602.12301 [cs.SD] (or arXiv:2602.12301v1 [cs.SD] for this version) https://doi.org/10.48550/arXiv.2602.12301 Focus to learn more arXiv-issued...
