Individual genre: how recommendations narrow the scene for music discovery practices

Abstract

There are a growing number of platform business models that offer digital infrastructures that connect various stakeholders, where data, and therefore user engagement, plays a pivotal role in sustaining and evolving the platform network. Recommendations sort out any information deemed irrelevant to the individual platform user, and thereby guide users through any information overflow. To date, few studies have investigated the interrelation between recommendations and user practices. In addition, there seems to be an underlying assumption in previous research that the intense rate of recommendations, i.e. how sensitive recommendations are and how fast they are updated, is exclusively positive. To address these gaps, this paper focuses on how recommendations shape music streaming discovery practices by placing user practices as the central unit of analysis, with a new lens on the agency of recommendations. Drawing on 15 semi-structured interviews and app walk-alongs with Spotify users, the paper shows how recommendations, in interplay with four different practice elements, narrow music streaming discovery to music that is adapted and restricted to what the user is predicted to like based on previous interactions, giving rise to the phenomenon that we term individual genre. The paper nuances our understanding of how recommendations influence user practices by shifting focus from user perceptions to a focus on practice, by taking into account a long-term perspective on user experience, and by problematizing previous assumptions regarding recommendation-based consumption. This ultimately offers an alternative view of how to create additional user value.