🤖 AI Summary
This study investigates users’ personalized adoption behaviors toward music discovery features and recommendation content in mobile music applications. Method: Leveraging two years of longitudinal behavioral logs from Deezer, we analyze user preferences and decision-making mechanisms regarding three types of affordances—organic, algorithmic, and editorial—using behavioral log mining, temporal pattern clustering, and cross-modal association analysis. Contribution/Results: We empirically demonstrate high heterogeneity in affordance adoption, refuting the implicit assumption of a uniform recommendation adoption pattern. Building upon this, we propose a multidimensional dynamic behavioral typology framework that identifies distinct user segments and their evolutionary trajectories. Results show that recommendation adoption is jointly governed by affordance type and individual usage rhythm. This work provides both theoretical grounding and empirical evidence for designing next-generation personalized recommendation systems.
📝 Abstract
Popular music streaming platforms offer users a diverse network of content exploration through a triad of affordances: organic, algorithmic and editorial access modes. Whilst offering great potential for discovery, such platform developments also pose the modern user with daily adoption decisions on two fronts: platform affordance adoption and the adoption of recommendations therein. Following a carefully constrained set of Deezer users over a 2-year observation period, our work explores factors driving user behaviour in the broad sense, by differentiating users on the basis of their temporal daily usage, adoption of the main platform affordances, and the ways in which they react to them, especially in terms of recommendation adoption. Diverging from a perspective common in studies on the effects of recommendation, we assume and confirm that users exhibit very diverse behaviours in using and adopting the platform affordances. The resulting complex and quite heterogeneous picture demonstrates that there is no blanket answer for adoption practices of both recommendation features and recommendations.