Singing Style Representation and User Preference Modeling for Music Recommendation Systems
Keywords:
singing style representation; music recommendation; user preference modeling; audio representation; fairness; socio-technical infrastructureAbstract
Music recommendation systems have traditionally relied on collaborative filtering and coarse audio features, which often fail to capture the expressive dimensions of singing voice that shape listener preference. This paper presents a system-level analysis of singing style representation and user preference modeling for music recommendation. It examines how vocal timbre, phonation, articulation, dynamics, ornamentation, and language-specific performance conventions can be represented through modern self-supervised audio encoders and sequence models. The discussion emphasizes architectural trade-offs between granularity, alignment, invariance, and interpretability in style representations. It further explores how user preference models can integrate explicit and implicit signals, contextual factors, cultural identity, and longitudinal taste evolution. The paper analyzes deployment considerations including multi-stage retrieval, embedding storage, latency, catalog updates, robustness to acoustic variability, and sustainability. Governance and fairness concerns are examined through the lenses of popularity bias, demographic bias, calibration, feedback loops, and sociotechnical abstraction. Policy implications for accountability, transparency, and cultural equity are considered. The paper argues that singing style should be treated as a first-class signal in music recommendation infrastructures, requiring careful coordination among representation learning, preference inference, platform governance, and evaluation practice.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
