2 Commits

Include renames

Author	SHA1	Message	Date
mrleo1nidandClaude Opus 5.5	2285107d04	test: end-to-end user journey in CI; fix ALS vectors for small libraries ... - 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) ... - 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