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Recommender systems, representativeness, and online music: a psychosocial analysis of Italian listeners

Year: 2026

Type: article

Abstract: Recommender systems shape music listening worldwide due to their widespread adoption on online platforms. Growing concerns about representational harms that these systems may cause are increasingly part of the scientific and public debate, wherein music listener perspectives are oftentimes reported and discussed, but rarely contextualised through a psychosocial and cultural lens. We address this gap by interviewing a group of Italian music listeners and analysing their narratives through Emotional Textual Analysis. Our findings reveal that listeners often engage with platforms in routinized ways, yet lack a critical understanding of how recommender systems operate and experience a sense of detachment from algorithmic processes. Moreover, while listeners perceive cultural and linguistic distinctions in music, their awareness of gender-related representational issues remains relatively limited. Overall, results underscore the importance of integrating psychosocial insights with technical approaches in the design of trustworthy and culturally sensitive music recommender systems.

Source: Humanities and Social Sciences Communications

DOI: 10.1057/s41599-026-07044-y

Authors: Lorenzo Porcaro, Chiara Monaldi

Institution: Sapienza University of Rome

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDG: Reduced inequalities

Open Access status: gold

Estimated APC paid: $1,890