Build a persistent, decaying model of each user's taste, recomputed daily,
that later phases consume across every recommendation surface. Phase 1 only
BUILDS the object — no behaviour change to what's surfaced yet.
Core mechanic — graded engagement (replaces binary was_skipped for learning;
was_skipped stays for History): a play's completion ratio maps to a signal in
[-1,+1] via two linear ramps (instant-skip → -1, ~0.30 neutral, ≥0.90 → +1).
Time-decayed (half-life ~75d) so recent behaviour dominates and the profile
tracks drift.
Per operator constraints:
- No explicit dislike button — negatives come only from passive behaviour
(early skips). Nothing recorded to regret or opt out of.
- Negatives are track-scoped; artist/tag weight is the decayed SUM of their
tracks' engagement, so one skip nets out against many good plays (a
DB test asserts a liked artist stays positive despite an early-skipped
track). A floor clamp bounds how negative any single entity can get.
- migration 0035: taste_profile_artists / taste_profile_tags (signed weight,
indexed by (user, weight DESC)).
- internal/taste: engagement.go (pure curve + decay) + profile.go
(accumulate plays + like bonuses, floor damping, size caps, atomic-replace).
- scheduler: rebuildUserDaily recomputes the profile before the playlist
build (so phase 2 can read it), best-effort — a taste failure never blocks
playlist building. Wired into the daily job + startup catch-up only (not
manual/lazy rebuilds).
- tests: pure (engagement curve, decay, ranking, floor, genre split) +
DB-backed (positive/negative weights, aggregation-protects-artist, like
bonus, atomic replace). All green vs real Postgres.
Config knobs live in taste.DefaultConfig() for now; wiring them into the
server RecommendationConfig is a later follow-up.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
bvandeusen
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