Emotion-Based Music Recommendation from Quality Annotations and Large-Scale User-Generated Tags
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization (UMAP 2024)
Original Kurzfassung:
Emotions constitute an important aspect when listening to music. While manual annotations from user studies grounded in psychological research on music and emotions provide a well-defined and fine-grained description of the emotions evoked when listening to a music track, user-generated tags provide an alternative view stemming from large-scale data. In this work, we examine the relationship between these two emotional characterizations of music and analyze their impact on the performance of emotion-based music recommender systems individually and jointly. Our analysis shows that (i) the agreement between the two characterizations, as measured with Cohen?s ? coefficient and Kendall rank correlation, is often low, (ii) Leveraging the emotion profile based on the intensity of evoked emotions from high-quality annotations leads to performances that are stable across different recommendation algorithms; (iii) Simultaneously leveraging the emotion profiles based on high-quality and large-scale annotations allows to provide recommendations that are less exposed to the low accuracy that algorithms might reach when leveraging one type of data, only.
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