Centralizes user-level features (completion_rate_30d, dismiss_rate_30d, mean_dwell_ms_30d, preferred_hour, tip_volume_30d) in a TS registry that owns both definition and SQL aggregation, since the data lives in the TS-owned SQLite tables (tip_views/tip_feedback). Lazy TTL refresh keeps recommend latency bounded; values persist in user_profile_features (KV). ml/serving accepts profile_features on /score + /generate but does not yet consume them — extending the bandit feature vector changes D and resets every user's learned state, so that's a deliberate phase-B step. Includes ml/features/profile_schema.py as a contract mirror with a sync test that diffs name sets against registry.ts. ADR-0011 records the data-locality reasoning (registry in TS, not Python as the issue originally suggested). Phase B (deferred): event-driven incremental updates, bandit consumption with state migration, admin per-user profile page, staleness alerts. Refs #81. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
1.6 KiB
1.6 KiB