bvandeusen
6e0d0e5723
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>
34 lines
1.4 KiB
SQL
34 lines
1.4 KiB
SQL
-- 0035_taste_profile.up.sql — per-user taste profile (#796 phase 1).
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--
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-- A persistent, decaying model of each user's taste, rebuilt daily from
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-- graded play engagement (completion + skip position) plus explicit
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-- likes. Two facet tables: weighted artists and weighted genre tags.
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-- Weight is a signed float — positive = drawn to, negative = (passively)
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-- avoided; magnitude reflects decayed engagement. Phase 1 only BUILDS
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-- these; consumption (candidate sourcing + scoring) lands in phase 2.
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--
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-- CASCADE on user/artist deletes keeps them clean. Indexed by
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-- (user_id, weight DESC) to serve "top taste artists/tags" reads cheaply.
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CREATE TABLE taste_profile_artists (
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user_id uuid NOT NULL REFERENCES users(id) ON DELETE CASCADE,
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artist_id uuid NOT NULL REFERENCES artists(id) ON DELETE CASCADE,
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weight double precision NOT NULL,
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updated_at timestamptz NOT NULL DEFAULT now(),
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PRIMARY KEY (user_id, artist_id)
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);
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CREATE INDEX taste_profile_artists_user_weight_idx
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ON taste_profile_artists (user_id, weight DESC);
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CREATE TABLE taste_profile_tags (
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user_id uuid NOT NULL REFERENCES users(id) ON DELETE CASCADE,
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tag text NOT NULL,
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weight double precision NOT NULL,
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updated_at timestamptz NOT NULL DEFAULT now(),
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PRIMARY KEY (user_id, tag)
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);
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CREATE INDEX taste_profile_tags_user_weight_idx
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ON taste_profile_tags (user_id, weight DESC);
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