mrleo1nid/pipu-music
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3 Commits
Author SHA1 Message Date
mrleo1nidandClaude Opus 5.5 ff0aafdb1e feat(recommender): Thompson-sampling bandit over candidate sources (M6)
ci / recommender / recommender (push) Failing after 4s
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ci / db / db (push) Failing after 2m21s
Details 
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>
 2026-10-20 22:31:24 +03:00
mrleo1nidandClaude Opus 5.5 2285107d04 test: end-to-end user journey in CI; fix ALS vectors for small libraries
ci / api / api (push) Successful in 30s
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ci / recommender / recommender (push) Successful in 30s
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ci / db / db (push) Failing after 1m8s
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- newApp (extracted from main) is the exact production wiring; the
  `ci / db` job now also runs it on PostgreSQL, Redis and Meilisearch
  and walks the main user flows over HTTP: first admin sign-in and
  credential change, local source and scan, search, album / artist /
  top tracks, signed stream, events and history, likes, fallback wave,
  playback state, upload queue up to review.
- ALS on a small library produced fewer than 64 factors and the insert
  into vector(64) failed; vectors are now zero-padded to the column size.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
 2026-10-20 18:26:36 +03:00
mrleo1nidandClaude Opus 5.5 3afed02f23 feat(recommender): nightly ALS with offline metrics and CF candidates in the wave (M6)
ci / recommender / recommender (push) Successful in 37s
Details 
- 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>
 2026-10-20 17:54:25 +03:00