- Introduced a new warning for users uploading non-FLAC or WAV formats, indicating that these formats should only be used as a last resort.
- Added functionality to highlight files needing format attention in the upload queue.
- Updated the UI to provide clearer guidance on acceptable audio formats during uploads.
- Improved documentation to reflect changes in upload requirements and user interface updates.
The library page compares listen-through, early-skip and like rates per
impression model version, so v1 and the trained ranker can be compared online.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
The wave logs each served candidate's feature vector to rec_impression_features.
Nightly training joins them with impression outcomes, validates on the last week
and activates the model only when it beats the v1 order and the active model.
The service loads the active ranker and uses its order in place of the v1 score.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
- GET /onboarding offers the library's top artists (with album covers)
and genres and says whether to show it (not done and < 10 plays);
PUT /me/onboarding stores the choice or a skip (migration 00009).
- The recommender seeds the taste from it while the user has fewer than
30 scored tracks: chosen artists' tracks +1, chosen genres +0.5.
- Web: onboarding replaces the wave on the home page until done or
skipped; artist grid with covers, genre chips, at least 3 artists.
- e2e journey and the recommender DB test cover it.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Nightly Beta(α, β) per user and source from wave impressions (reward:
complete or like, ×0.99 daily decay) stored in user_profiles_rec.bandit;
the wave adds 0.2·(θ − 0.5) per candidate source, sources without data
stay neutral. The user's exploration share now follows how well `fresh`
does against the other sources.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
- Implicit scores from play events and reactions per the signals table
(90-day half-life, weaker skips outside the wave, dislikes override).
- numpy ALS for implicit feedback; time-split evaluation on the last
7 days (NDCG@10, Recall@50) against a popularity baseline; a new model
is activated only if NDCG@10 does not regress by more than 2%.
- Vectors are written to track_embeddings.cf and user_profiles_rec.taste_cf,
versions to model_versions; NightlyTrainer runs at TRAIN_HOUR and on
first start, `pipu-rec train` runs it manually.
- Wave: cf and item_cf candidate sources and a 0.10 cf term in the v1
score; impressions are tagged heuristic-v1+cf.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
mrleo1nid
