4 Commits

Include renames

Author	SHA1	Message	Date
mrleo1nidandClaude Opus 5.5	58bd14ef38	feat(recommender): LightGBM lambdarank ranker with impression features (M6) ... 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>	2026-10-20 22:41:26 +03:00
mrleo1nidandClaude Opus 5.5	ff0aafdb1e	feat(recommender): Thompson-sampling bandit over candidate sources (M6) ... 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 ... - 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