Interactive Explanatory Recommender System
Overview
Blooming Beats transforms a decade of personal Spotify listening data into visual narratives grounded in Data Humanism principles.
Music is among the most personal data a person generates. A listening history is not a log of events — it is a record of moods, memories, relationships, and moments in time. Yet streaming platforms treat this record as a raw signal for optimization.
"What if a music recommender system treated listening histories not as input for a model, but as material for a story?"Research question driving Blooming Beats
The Problem
A song played during a period of grief, an album associated with a particular summer, a playlist assembled for a journey — all carry meaning that play counts and timestamps cannot encode. Existing platforms treat listening data as a behavioral signal and nothing more. Spotify's annual "Wrapped" offers statistical aggregations, but provides no mechanism for users to interrogate, annotate, or make personal meaning visible.
Music recommendation research has focused primarily on model transparency — revealing feature weights and genre-based rationales — but rarely grounds explanations in the user's personal history. Recommender systems position users as decision-makers evaluating algorithmic output, not as people whose lives and stories the system might reflect. Users want to understand the connection, not the algorithm.
Research Foundation
Blooming Beats is grounded in the conviction that the principles Giorgia Lupi developed for making personal data visible are the same principles that should drive how recommender systems explain themselves. Each Data Humanism principle directly addresses a structural failure in how music recommendation currently works.
Small Data
This principle arguments that individual, intimate datasets — a person's specific history — can reveal what population-scale statistics erase. The personal record is irreducible and meaningful on its own terms.
Explanation Aim & Personalization
One user's 10-year archive shifts the explanation aim from "why the algorithm chose this" to "how your own listening history connects to this song." Personalization is not a feature layer, it is the entire dataset.
Subjective Data
The principle that moods, relationships, and memories encoded in personal data are valid and not noise to normalize. Subjectivity carries meaning that objective analysis loses.
Personalization
Personal memory and emotional association become first-class explanation inputs. The annotation system makes the user's subjective layer the primary explanatory layer, not supplementary decoration.
Design-Driven Data
The commitment to establishing a visual metaphor before building the data pipeline, letting form drive methodology rather than emerging as a styling decision after the fact.
Cognitive Load
The visual vocabulary makes the recommendation immediately perceivable without reading text. The feature values are read as visual rhythms rather than numbers where the form reduces the cognitive cost of understanding the explanation.
Data to Depict Complexity
The commitment to rendering multiple data dimensions navigable progressively rather than collapsing them into a single summary view that erases the relationships between variables.
Output Format & Cognitive Load
The explanation operates at multiple output levels — overlaid flower comparison, three impact scores, behavioral line encoding — navigable on demand. Complexity is distributed across layers, not hidden.
Spend Time with Data
The practice of slow, reflective engagement with personal archives, resisting the impulse toward rapid statistical consumption and inviting users to dwell within the data's temporal depth.
Cognitive Load & Personalization
Three temporal views (day, week, month) distribute attention across a 10-year archive rather than overwhelming with everything at once. Exploration surfaces pattern while selection turns those patterns into recommendations.
Serendipitous Data
The recognition that unexpected patterns are meaningful signals worth surfacing, not anomalies to suppress or errors to correct.
Output Format
Unexpected connections across the archive are rendered as navigable recommendation pathways, extending the output format beyond nearest-neighbor retrieval to temporal discovery across a decade of listening.
Methodology
Visual Language
Each song is represented as a flower-like graph with six petals — one for each selected audio feature. The number of colored dots forming each petal encodes the feature value: a danceability score of 0.780 becomes 78 dots. This choice enables natural clustering that emerges from similar dot densities, matching the rhythmic nature of music itself.
The visual metaphor emerged during the data sketching stage, drawing from Giorgia Lupi's "Bruises — The Data We Don't See" and the OECD Better Life Index.
Evaluation
Four participants from diverse academic backgrounds explored the listening archive of an unfamiliar user, identified a personal pattern, generated recommendations, and engaged with the explanation view The task was designed to test whether the narrative and explanation design remain legible to someone who did not produce the data.
All four participants formed connections between songs and memories — concerts, milestones, seasons, sensations. Yet three out of four did not actively track them, describing the effort as overwhelming. The desire to engage with listening history as personal narrative exists, although the cost of doing so manually is prohibitive.
Participants demonstrated three distinct approaches: high contextualization (always wanting others to know why a song matters), moderate (selective sharing), and none (sharing without commentary). The three-level contextual architecture accommodates all three without imposing a single mode of engagement.
Participants did not want to understand the model. They wanted to understand why this song, why now, in relation to what they had already listened to. The flower comparison view — overlaying the aggregated profile of selected songs against the recommendation — delivers exactly that: a visual argument, not a technical disclosure.
Publication
Blooming Beats contributes a novel personal data visualization approach that transforms
music listening histories into personal narratives. The flower-based visual dictionary,
the three-level contextual enrichment architecture, and the temporal multi-view design
collectively demonstrate how Data Humanism principles can be operationalized in an
interactive music recommender system.
By treating explanation not as a transparency mechanism but as a narrative layer of
the user experience, Blooming Beats marks a shift in how listening histories are
conceptualized: as narrative material rather than behavioral traces or optimization targets.