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This study introduces a novel approach to music recommendation by leveraging critical adjacency鈥攔elationships established by expert critics between artists鈥攁longside traditional user-item interactions and intrinsic musical content. By employing marginal optimal-transport distances to analyze acoustic distributions of artists, the authors validate that critical adjacency can be effectively recovered from sonic features, achieving an out-of-sample AUC of 0.767. The findings reveal that critical discourse not only enhances recommendation systems but also provides insights into the sociological context of music genres, indicating a richer understanding of artist relationships beyond mere acoustic similarity.
Critical adjacency from expert critics can significantly enhance music recommendation systems, achieving a notable AUC of 0.767 in cold-start scenarios.
Music recommendation relies primarily on two signals: user-item interactions, which fail in the cold-start regime, and intrinsic musical content, available for any recording. We argue that a third, largely untapped signal is both richer and more principled: critical adjacency, the pairwise relation established when an expert critic explicitly links two artists in long-form prose. It encodes deliberate judgments about which artists belong together. Prior work established its internal validity, showing it recovers coherent, interpretable communities and can match collaborative filtering in user-satisfaction simulations, with no user data. What has been missing is external validation: whether this critic-sourced relation is grounded in the music itself versus sociological context. We test it against acoustic content, reframing the question as one of construct validity. Representing artists as empirical distributions over 80 low-level Essentia acoustic descriptors and modeling pairwise proximity via marginal optimal-transport (Wasserstein) distances, we evaluate how far critical adjacency is sonically recoverable under a cold-start, artist-disjoint split. Our ensemble recovers these edges at out-of-sample AUC of 0.767 (95% CI 0.761-0.775). Recoverability rises monotonically with critical consensus, reaching 0.865 on multi-source attested edges. Stratified evaluations align with sociological models of genre: tightly bounded, scene-based genres show higher recoverability than broad industry umbrella terms. Critical discourse is thus a rich source of information for recommendation, decomposing into a reproducible"sonic core"and a"sociological remainder"driven by narrative positioning, subcultural context, and canonical placement. The work offers both a scalable cold-start discovery mechanism and a sociologically grounded approach to MIR and MRS research.
