Tagged at first listen: an examination of social tagging practices in a music recommender system
Bibliographic record
Abstract
Social tagging has become a very common way to index different types of resources on the web. Less prevalent in music than in other domains, social tagging is nevertheless used in a popular recommender system, Last.fm. Although the number of publications on tagging and folksonomies has exploded in the last few years, music tagging is still not well studied. In this paper, we present a study of tagging practices of Last.fm users. We examine the social tagging of songs during the first three months after their release. Our analysis shows that the release of a song triggers a burst in tagging activity that lasts two weeks, after what it decreases sharply and then remains fairly constant for the next ten weeks. We also find that a majority of songs do not get tagged during the first week and that tagging was positively related to popularity. Finally, we find that tags that have been frequently applied to a given song are more likely to be genre related, shorter in length, and relatively objective than tags that have been applied only once.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".