Beyond the Algorithm: How Users Interpret and Navigate Music Recommendation Systems on Streaming Platforms

This thesis explores how users of music streaming platforms interpret and navigate their algorithmically mediated listening experiences. While much of the existing research foregrounds the technical design of these systems and their broader implications on music consumption, this study shifts the focus to the listener's perspective. Drawing on semi-structured interviews with 14 participants from Italy and Sweden, the research explores how individuals make sense of algorithmic recommendations in relation to their listening habits, sense of autonomy, and evolving identities as listeners. Employing inductive thematic analysis, this study identifies recurring patterns in how users engage with algorithmic curation. The analysis highlighted tensions between trust and skepticism, serendipity and control, and automation and self-expression on the part of the users. The findings reveal that users actively engage with music recommender systems (MRS), at times embracing, questioning, or often adapting them to fit their personal listening routines. Participants frequently described their emotions in relations to their experiences on music streaming platforms, highlighting the importance of considering the emotional and interpretative layers in the elaboration of algorithmic mediation experiences. By foregrounding the perspectives of users, this thesis contributes to a more granular understanding of how recommendation algorithms are experienced in everyday musical life. It underscores the agency of listeners, considering them as critical actors who interpret, appropriate, and at times resist algorithmic influence in ways that reflect both personal and cultural meanings of music. Finally, this research aims not only at deepening our understanding of how users engage with algorithmic systems, but also at informing more nuanced, human-centered approaches across disciplines. In addition, it seeks to encourage methodologies and designs that account for lived experience, support user autonomy, and critically reflect on the cultural and psychological dimensions of algorithmic mediation.