Blooming Beats: An interactive explainable recommender system for exploring personal music narratives through data humanism principles
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Author (Corporation)
Publication date
06.10.2025
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Collections
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04B - Conference paper
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Parent work
Proceedings of the 16th Biannual Conference of the Italian SIGCHI Chapter
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Pages / Duration
1-15
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ACM
Place of publication / Event location
New York
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Abstract
Recommender systems have become integral to our daily digital experiences, particularly in music streaming services. However, existing systems often reduce rich listening experiences to mere analytics, overlooking the personal narratives and contexts that make music meaningful. We present Blooming Beats, an interactive explainable recommender system that transforms a decade of personal Spotify listening data into compelling visual narratives. Grounded in Data Humanism principles, it represents songs as flower-like graphs where petals encode audio features, while connecting lines and contextual markers capture listening behaviors, personal milestones, and global events. The system enables users to explore 10 years of listening history through multiple temporal views, re-live musical moments, and generate context-aware recommendations. Through a preliminary qualitative user study, we found that Blooming Beats enables users to explore listening histories and understand recommendations in tailored and contextualized ways, demonstrating how Data Humanism can be employed to engage with personal data to enhance music recommendation explainability.
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Event
16th Biannual Conference of the Italian SIGCHI Chapter (CHItaly '25)
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Conference start date
06.10.2025
Conference end date
10.10.2025
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ISBN
979-8-4007-2102-1
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Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
Peer review of the complete publication
Open access category
Hybrid
Citation
Al-Hazwani, I., Ahmed, N., Fedosov, A., Aschwanden, O., Huber, L., Lutziger, D., Kirchdorfer, C., & Bernard, J. (2025). Blooming Beats: An interactive explainable recommender system for exploring personal music narratives through data humanism principles. Proceedings of the 16th Biannual Conference of the Italian SIGCHI Chapter, 1–15. https://doi.org/10.1145/3750069.3750396