3 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	fb07f962eb	feat: taste onboarding for new listeners (M6) ... - 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>	2026-10-20 22:35:43 +03:00
mrleo1nidandClaude Opus 5.5	f5c276092f	ci: integration tests on PostgreSQL + pgvector for migrations and store SQL ... New `ci / db` job with a pgvector service: Go applies all migrations, rolls them back to zero and applies them again, then runs the scanner, fingerprint, write-template, top-tracks and upload-queue store queries; Python runs the analysis worker, catalog / profile loading and a full ALS training run against the same schema. Until now this SQL ran nowhere before reaching a real server. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>	2026-10-20 18:18:44 +03:00