Why Did the Algorithm Think I'd Like This? Understanding Behavioral Signals in Personalized Music Recommendation

Research archive for the paper Why Did the Algorithm Think I'd Like This? Understanding Behavioral Signals in Personalized Music Recommendation (Shuvi, in submission to the Journal of Open Humanities Data). The archive contains the unified 22-scorer evaluation kernel (lb-eval v5.0.0), its v6 evidence outputs (metrics, analysis, multiseed, global-split, report), and the manuscript source and PDF. Evaluated on sanitized MLHD+ (1.68 billion listening events, 36,970 users, 2.8M recordings). Contents: paper/ (manuscript), code/ (lb_eval.py v5.0.0), evidence/ (kernel v6 outputs), checksums/SHA256SUMS.txt, PROVENANCE.md (source commit, kernel versions, related DOIs), CITATION.cff. Companion records: MLHD+ audit archive 10.5281/zenodo.22338293; Euterpe software archive 10.5281/zenodo.22665816; model artifacts at huggingface.co/artlostintime/euterpe-model.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-09
DOI
https://doi.org/10.5281/zenodo.22661886
Primary Topic
Music and Audio Processing
Type
article
Field-Weighted Citation Impact
0.00
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article

Why Did the Algorithm Think I'd Like This? Understanding Behavioral Signals in Personalized Music Recommendation

Shuvi
Zenodo (CERN European Organization for Nuclear Research)
Music and Audio Processing
article

Why Did the Algorithm Think I'd Like This? Understanding Behavioral Signals in Personalized Music Recommendation

Shuvi
article en

Abstract

Research archive for the paper Why Did the Algorithm Think I'd Like This? Understanding Behavioral Signals in Personalized Music Recommendation (Shuvi, in submission to the Journal of Open Humanities Data). The archive contains the unified 22-scorer evaluation kernel (lb-eval v5.0.0), its v6 evidence outputs (metrics, analysis, multiseed, global-split, report), and the manuscript source and PDF. Evaluated on sanitized MLHD+ (1.68 billion listening events, 36,970 users, 2.8M recordings). Contents: paper/ (manuscript), code/ (lb_eval.py v5.0.0), evidence/ (kernel v6 outputs), checksums/SHA256SUMS.txt, PROVENANCE.md (source commit, kernel versions, related DOIs), CITATION.cff. Companion records: MLHD+ audit archive 10.5281/zenodo.22338293; Euterpe software archive 10.5281/zenodo.22665816; model artifacts at huggingface.co/artlostintime/euterpe-model.

Zenodo (CERN European Organization for Nuclear Research)
Quality Education
Openalex Percentile: Top 9%
Music and Audio Processing
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