feat(taste): phase 2a — apply the taste profile via a TasteMatch scoring term (#796)

The profile built in phase 1 now changes what gets surfaced. Adds a TasteMatch
term to the weighted-shuffle score so candidates are re-ranked by their fit to
the user's learned taste (positive draws toward it; negative reflects passive
avoidance; 0 at cold start).

- recommendation/score.go: ScoringInputs.TasteMatchScore ([-1,+1]) +
  ScoringWeights.TasteWeight + the term in Score.
- recommendation/taste.go: LoadTasteProfile reads the taste_profile_* tables;
  TasteProfile.Match blends the candidate's artist weight (0.7) and avg genre-tag
  weight (0.3), each tanh-squashed by a fixed scale so one outlier artist can't
  compress the rest. Unknown artist/tags and empty profiles → 0 (neutral).
- candidates.go: both candidate loaders set TasteMatchScore per candidate, so
  every Score caller (system playlists incl. You-might-like, radio) becomes
  taste-aware automatically.
- weights: systemMixWeights.TasteWeight = 1.5 (daily mixes are the primary
  taste surface); config.RecommendationConfig gains taste_weight (default 1.0,
  lighter — radio is seed-directed) wired into the radio handler.
- tests: pure (Match curve incl. saturation/clamp/empty-neutral, Score term
  add+subtract) + DB round-trip (seed taste rows → Match positive). All green
  vs real Postgres; existing playlist/radio tests unaffected (empty profile →
  zero taste effect).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

internal/api/radio.go

@@ -108,6 +108,7 @@ func (h *handlers) handleRadio(w http.ResponseWriter, r *http.Request) {
 		JitterMagnitude:  h.recCfg.JitterMagnitude,
 		ContextWeight:    h.recCfg.ContextWeight,
 		SimilarityWeight: h.recCfg.SimilarityWeight,
+		TasteWeight:      h.recCfg.TasteWeight,
 	}
 	picks := recommendation.Shuffle(candidates, weights, time.Now().UTC(), h.rng, limit-1)
 

internal/config/config.go

@@ -98,6 +98,7 @@ type RecommendationConfig struct {
 	JitterMagnitude     float64 `yaml:"jitter_magnitude"`
 	ContextWeight       float64 `yaml:"context_weight"`
 	SimilarityWeight    float64 `yaml:"similarity_weight"`
+	TasteWeight         float64 `yaml:"taste_weight"`
 	RecentlyPlayedHours int     `yaml:"recently_played_hours"`
 	RadioSize           int     `yaml:"radio_size"`
 	RadioSizeMax        int     `yaml:"radio_size_max"`
@@ -119,13 +120,16 @@ func Default() Config {
 			SkipMaxDurationPlayedMs: 30000,
 		},
 		Recommendation: RecommendationConfig{
-			BaseWeight:          1.0,
-			LikeBoost:           2.0,
-			RecencyWeight:       1.0,
-			SkipPenalty:         1.0,
-			JitterMagnitude:     0.1,
-			ContextWeight:       2.0,
-			SimilarityWeight:    2.0,
+			BaseWeight:       1.0,
+			LikeBoost:        2.0,
+			RecencyWeight:    1.0,
+			SkipPenalty:      1.0,
+			JitterMagnitude:  0.1,
+			ContextWeight:    2.0,
+			SimilarityWeight: 2.0,
+			// Radio is seed-directed (the user picked a direction), so taste
+			// is a lighter nudge here than in the daily mixes (1.5).
+			TasteWeight:         1.0,
 			RecentlyPlayedHours: 1,
 			RadioSize:           50,
 			RadioSizeMax:        200,

internal/playlists/system.go

@@ -148,6 +148,11 @@ var systemMixWeights = recommendation.ScoringWeights{
 	JitterMagnitude:  0.1,
 	ContextWeight:    0.5,
 	SimilarityWeight: 1.5,
+	// Taste profile (#796 phase 2): the daily mixes are the primary
+	// taste-driven surface, so they lean on it. TasteMatchScore is in
+	// [-1,+1], so 1.5 makes a strong taste fit comparable to a like boost
+	// while passive avoidance (negative) gently demotes.
+	TasteWeight: 1.5,
 }
 
 // forYouHeadN is the number of top-scored tracks that anchor the For-You

internal/recommendation/candidates.go

@@ -36,6 +36,11 @@ func LoadCandidates(
 		return nil, err
 	}
 
+	profile, err := LoadTasteProfile(ctx, q, userID)
+	if err != nil {
+		return nil, err
+	}
+
 	out := make([]Candidate, 0, len(rows))
 	for _, r := range rows {
 		var lpt *time.Time
@@ -52,6 +57,7 @@ func LoadCandidates(
 				PlayCount:            int(r.PlayCount),
 				SkipCount:            int(r.SkipCount),
 				ContextualMatchScore: ctxScore,
+				TasteMatchScore:      profile.Match(r.Track.ArtistID, r.Track.Genre),
 			},
 		})
 	}
@@ -117,6 +123,11 @@ func LoadCandidatesFromSimilarity(
 		return nil, err
 	}
 
+	profile, err := LoadTasteProfile(ctx, q, userID)
+	if err != nil {
+		return nil, err
+	}
+
 	out := make([]Candidate, 0, len(rows))
 	for _, r := range rows {
 		var lpt *time.Time
@@ -141,6 +152,7 @@ func LoadCandidatesFromSimilarity(
 				SkipCount:            int(r.SkipCount),
 				ContextualMatchScore: ctxScore,
 				SimilarityScore:      simScore,
+				TasteMatchScore:      profile.Match(r.Track.ArtistID, r.Track.Genre),
 			},
 		})
 	}

internal/recommendation/score.go

@@ -19,6 +19,11 @@ type ScoringInputs struct {
 	SkipCount            int        // play_events with was_skipped=true
 	ContextualMatchScore float64    // [0, 1]; 0 when no signal
 	SimilarityScore      float64    // [0, 1]; 0 when no signal (random fill)
+	// TasteMatchScore is the candidate's fit to the user's learned taste
+	// profile (#796 phase 2), in [-1, +1]: positive draws a track toward the
+	// user's taste, negative reflects passive avoidance, 0 when there's no
+	// profile signal (cold start / artist+tags absent from the profile).
+	TasteMatchScore float64
 }
 
 // ScoringWeights are the operator-tunable knobs. Defaults live in
@@ -31,6 +36,7 @@ type ScoringWeights struct {
 	JitterMagnitude  float64
 	ContextWeight    float64
 	SimilarityWeight float64
+	TasteWeight      float64
 }
 
 // Score computes the weighted-shuffle score per spec §6:
@@ -41,6 +47,7 @@ type ScoringWeights struct {
 //	      - skip_ratio * SkipPenalty
 //	      + contextual_match_score * ContextWeight
 //	      + similarity_score * SimilarityWeight
+//	      + taste_match_score * TasteWeight
 //	      + small_random_jitter
 //
 // Higher score = more likely to surface. rng is a function returning a
@@ -55,6 +62,7 @@ func Score(in ScoringInputs, w ScoringWeights, now time.Time, rng func() float64
 	s -= skipRatio(in.PlayCount, in.SkipCount) * w.SkipPenalty
 	s += in.ContextualMatchScore * w.ContextWeight
 	s += in.SimilarityScore * w.SimilarityWeight
+	s += in.TasteMatchScore * w.TasteWeight
 	s += (rng()*2 - 1) * w.JitterMagnitude
 	return s
 }

internal/recommendation/taste.go

@@ -0,0 +1,91 @@
+package recommendation
+
+import (
+	"context"
+	"math"
+
+	"github.com/jackc/pgx/v5/pgtype"
+
+	"git.fabledsword.com/bvandeusen/minstrel/internal/db/dbq"
+)
+
+// Taste-match tuning. The taste profile (written by internal/taste) holds
+// signed, unbounded artist/tag weights; these scales squash them into a
+// bounded [-1, +1] match via tanh, so one outlier artist can't compress the
+// rest toward zero (as a per-user max-normalisation would). A weight at the
+// scale value maps to tanh(1) ≈ 0.76 — "clearly a preference."
+const (
+	tasteArtistScale  = 4.0
+	tasteTagScale     = 3.0
+	tasteArtistShare  = 0.7
+	tasteTagShare     = 0.3
+	tasteProfileLimit = 2000 // read cap; profiles are size-capped on write
+)
+
+// TasteProfile is the read-side view of a user's learned taste: signed
+// weights over artists and genre tags. The zero value (and any unknown
+// artist/tag) contributes 0, so cold-start users get no taste effect.
+type TasteProfile struct {
+	artists map[pgtype.UUID]float64
+	tags    map[string]float64
+}
+
+// LoadTasteProfile reads the user's taste profile from the taste_profile_*
+// tables (written daily by internal/taste). Returns an empty profile with no
+// error when the user has none.
+func LoadTasteProfile(ctx context.Context, q *dbq.Queries, userID pgtype.UUID) (TasteProfile, error) {
+	arts, err := q.ListTasteProfileArtistsForUser(ctx, dbq.ListTasteProfileArtistsForUserParams{
+		UserID: userID, Limit: tasteProfileLimit,
+	})
+	if err != nil {
+		return TasteProfile{}, err
+	}
+	tags, err := q.ListTasteProfileTagsForUser(ctx, dbq.ListTasteProfileTagsForUserParams{
+		UserID: userID, Limit: tasteProfileLimit,
+	})
+	if err != nil {
+		return TasteProfile{}, err
+	}
+	p := TasteProfile{
+		artists: make(map[pgtype.UUID]float64, len(arts)),
+		tags:    make(map[string]float64, len(tags)),
+	}
+	for _, a := range arts {
+		p.artists[a.ArtistID] = a.Weight
+	}
+	for _, t := range tags {
+		p.tags[t.Tag] = t.Weight
+	}
+	return p, nil
+}
+
+// Match scores a candidate track's fit to the profile in [-1, +1]: a blend of
+// the artist's taste weight and the average of its genre tags' weights, each
+// tanh-squashed. Absent artist/tags contribute 0.
+func (p TasteProfile) Match(artistID pgtype.UUID, genre *string) float64 {
+	a := math.Tanh(p.artists[artistID] / tasteArtistScale)
+
+	var tg float64
+	if genre != nil {
+		tags := splitGenres(*genre)
+		if len(tags) > 0 {
+			var sum float64
+			for _, t := range tags {
+				sum += p.tags[t]
+			}
+			tg = math.Tanh((sum / float64(len(tags))) / tasteTagScale)
+		}
+	}
+	return clampUnit(tasteArtistShare*a + tasteTagShare*tg)
+}
+
+// clampUnit constrains x to [-1, 1].
+func clampUnit(x float64) float64 {
+	if x < -1 {
+		return -1
+	}
+	if x > 1 {
+		return 1
+	}
+	return x
+}

internal/recommendation/taste_test.go

@@ -0,0 +1,109 @@
+package recommendation
+
+import (
+	"context"
+	"math"
+	"testing"
+	"time"
+
+	"github.com/jackc/pgx/v5/pgtype"
+
+	"git.fabledsword.com/bvandeusen/minstrel/internal/db/dbq"
+)
+
+func uuidN(n byte) pgtype.UUID {
+	return pgtype.UUID{Bytes: [16]byte{15: n}, Valid: true}
+}
+
+func strPtr(s string) *string { return &s }
+
+func TestTasteProfile_Match(t *testing.T) {
+	loved := uuidN(1)
+	disliked := uuidN(2)
+	unknown := uuidN(3)
+	p := TasteProfile{
+		artists: map[pgtype.UUID]float64{loved: 8.0, disliked: -8.0},
+		tags:    map[string]float64{"Jazz": 6.0, "Noise": -6.0},
+	}
+
+	if m := p.Match(loved, strPtr("Jazz")); m <= 0.5 {
+		t.Errorf("loved artist + loved tag = %.3f, want strongly positive", m)
+	}
+	if m := p.Match(disliked, strPtr("Noise")); m >= -0.5 {
+		t.Errorf("disliked artist + disliked tag = %.3f, want strongly negative", m)
+	}
+	if m := p.Match(unknown, nil); m != 0 {
+		t.Errorf("unknown artist, no genre = %.3f, want 0", m)
+	}
+	// Artist dominates (0.7 share): loved artist with an unknown tag is still
+	// clearly positive.
+	if m := p.Match(loved, strPtr("Unheard")); m <= 0 {
+		t.Errorf("loved artist + unknown tag = %.3f, want positive", m)
+	}
+	// Output stays within [-1, 1] even with saturated inputs.
+	for _, a := range []pgtype.UUID{loved, disliked, unknown} {
+		m := p.Match(a, strPtr("Jazz"))
+		if m < -1 || m > 1 {
+			t.Errorf("Match out of [-1,1]: %.3f", m)
+		}
+	}
+}
+
+func TestTasteProfile_EmptyIsNeutral(t *testing.T) {
+	var p TasteProfile // zero value: nil maps
+	if m := p.Match(uuidN(1), strPtr("Jazz")); m != 0 {
+		t.Errorf("empty profile Match = %.3f, want 0 (cold start neutral)", m)
+	}
+}
+
+func TestScore_TasteTermAddsAndSubtracts(t *testing.T) {
+	now := time.Now()
+	zeroJitter := func() float64 { return 0.5 } // (0.5*2-1)=0 with any magnitude
+	w := ScoringWeights{TasteWeight: 2.0}       // all other weights 0
+
+	pos := Score(ScoringInputs{TasteMatchScore: 1.0}, w, now, zeroJitter)
+	if !almostEq(pos, 2.0) {
+		t.Errorf("positive taste: Score = %.3f, want 2.0", pos)
+	}
+	neg := Score(ScoringInputs{TasteMatchScore: -1.0}, w, now, zeroJitter)
+	if !almostEq(neg, -2.0) {
+		t.Errorf("negative taste: Score = %.3f, want -2.0 (demotes)", neg)
+	}
+	off := Score(ScoringInputs{TasteMatchScore: 1.0}, ScoringWeights{}, now, zeroJitter)
+	if !almostEq(off, 0.0) {
+		t.Errorf("TasteWeight 0: Score = %.3f, want 0 (no effect)", off)
+	}
+}
+
+func almostEq(a, b float64) bool { return math.Abs(a-b) < 1e-9 }
+
+// TestLoadTasteProfile_RoundTrip seeds taste_profile rows and verifies the
+// reader hydrates them into a profile that scores a matching track positively.
+func TestLoadTasteProfile_RoundTrip(t *testing.T) {
+	pool := newPool(t)
+	ctx := context.Background()
+	u := seedUser(t, pool, "taste-rt")
+	art := seedArtist(t, pool, "Loved Artist", "")
+
+	if _, err := pool.Exec(ctx,
+		`INSERT INTO taste_profile_artists (user_id, artist_id, weight) VALUES ($1, $2, $3)`,
+		u.ID, art.ID, 8.0); err != nil {
+		t.Fatalf("seed taste artist: %v", err)
+	}
+	if _, err := pool.Exec(ctx,
+		`INSERT INTO taste_profile_tags (user_id, tag, weight) VALUES ($1, $2, $3)`,
+		u.ID, "Jazz", 6.0); err != nil {
+		t.Fatalf("seed taste tag: %v", err)
+	}
+
+	p, err := LoadTasteProfile(ctx, dbq.New(pool), u.ID)
+	if err != nil {
+		t.Fatalf("load: %v", err)
+	}
+	if m := p.Match(art.ID, strPtr("Jazz")); m <= 0.5 {
+		t.Errorf("round-trip Match = %.3f, want strongly positive", m)
+	}
+	if m := p.Match(uuidN(9), nil); m != 0 {
+		t.Errorf("absent artist Match = %.3f, want 0", m)
+	}
+}