Identifying time zones in a large dataset of music listening logs
Bibliographic record
Abstract
Knowing where listeners are is an important contextual dimension that can be used in context-aware music recommendation systems to improve their performance. This paper presents our research on identifying the time zone where listeners are by analysing their weekly aggregated music listening profiles. We collected a large dataset of full music listening histories (N=594K) of users of the Last.fm's scrobbler service from all around the globe, and formulated six approaches for identifying the time zone where these listening profiles have been generated based on their listeners' behaviour. The performance of these approaches was compared with a manually labelled dataset of listening profiles' time zones. We found that the best method was based on the assumption that people, in general, sleep during night time and submit fewer music logs. This approach, implemented by estimating the local minima of people's weekly aggregated listening profile, resulted in a 75 percent correctly identified time zones with a tolerance of +/- 1 hour.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".