- 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>
- migration 00006: track features and embeddings (pgvector, HNSW),
analysis queue filled by a trigger on new tracks, wave sessions and
impressions, recommender profiles, model versions
- analyzer basic-1 (ADR-014): ffmpeg decode, three 30 s excerpts,
256-d timbre/rhythm embedding, tempo by onset autocorrelation, key,
loudness, energy, danceability, acousticness, intro length; worker
with SKIP LOCKED claims, retries and re-analysis on version change
- wave engine v1: candidates (session/taste similarity, fresh,
liked artists, rediscover, popular, exploration, seed), filters
(dislikes, played in 3 h, already served), heuristic score, activity
and mood fit from contexts.yaml with relaxing thresholds, MMR with
one track per artist, exploration share, session vector updated by
reactions and a change of course after three skips
- POST /internal/wave/batch with the catalog index cached in memory
and the session kept in Redis
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
mrleo1nidandClaude Opus 5.5