Beyond Musical Descriptors: Extracting Preference-Bearing Intent in Music Queries

2026-02-11

Technology Category

Machine Learning: Learning Preferences or RankingsData Mining & Knowledge Management: Intelligent Query ProcessingNatural Language Processing: Question Answering

Application Category

User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendationWeb Mining and Content Analysis: Large pretrained models with web dataSemantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• user intent
• music queries
• preference-bearing descriptors
• musical descriptors
• intent understanding

Innovation

• user intent
• music descriptors
• preference modeling
• large language models
• MusicRecoIntent