SEGMENTACIÓN ACÚSTICA BASADA EN K-MEANS PARA MITIGAR EL SESGO DE POPULARIDAD EN LAS RECOMENDACIONES MUSICALES
Resumen
En esta investigación, proponemos un Sistema de Recomendación Contextual basado en agrupamiento acústico. El modelo utiliza el algoritmo K-Means (k = 6) para segmentar un extenso catálogo musical de Spotify en perfiles de sonido estadísticamente distintos. La validación estadística mediante ANOVA unidireccional confirmó la distinción de los seis grupos (p < 0,001), lo que garantiza la robustez de la segmentación y revela la estructura de cola larga. El sistema asigna estos grupos a contextos de uso (p. ej., "Enfoque" → Grupo Instrumental) para mitigar el sesgo de popularidad inherente en los datos. Este enfoque ha demostrado su potencial para aumentar el valor de descubrimiento de las recomendaciones, promoviendo la diversidad y abordando las deficiencias de los sistemas basados únicamente en la popularidad.
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