The Influence of Recommendation Strategies and Music Diversity on User Preference: A Structural Equation Modeling Approach to Subjective and Objective Measures

ABSTRACT This study investigates how different music discovery approaches influence users' perceptions of playlist diversity and satisfaction, with a particular focus on both objective and subjective measures of musical diversity. A within‐subjects experiment involving 144 Spotify users compared two systems: a user‐driven seed‐based search and the algorithm‐driven Discover Weekly. Objective diversity was calculated through genre, artist, and Spotify audio features, while subjective diversity was measured using participants' self‐reported ratings. Results from structural equation modeling and t‐tests revealed that Discover Weekly consistently produced more diverse playlists across all objective measures. However, only genre and artist diversity significantly influenced users' perceived diversity; sonic diversity had a limited perceptual impact. Moreover, subjective diversity, rather than objective diversity, showed a stronger association with overall playlist satisfaction. These findings suggest that users' evaluations are shaped more by perceived categorical diversity than by measurable acoustic variance. Although familiarity influenced individual track preference, its effect on overall playlist satisfaction was weaker, implying the role of other mediating factors such as novelty or user traits. Overall, our findings reaffirm the need for exploration‐oriented measures that go beyond accuracy. Specifically, they underscore the importance of diversity in evaluating playlist‐based music recommendations.