feat(recommendation): extend Score with ContextualMatchScore + ContextWeight

internal/recommendation/score.go

@@ -8,12 +8,15 @@ import (
 )
 
 // ScoringInputs are the per-track facts the score function consumes.
-// Sub-plan #3 (contextual scoring) extends this with ContextualMatchScore.
+// ContextualMatchScore is in [0, 1] — max similarity between the user's
+// current session vector and any non-seed contextual_like row for this
+// track. Set by LoadCandidates after a bulk fetch.
 type ScoringInputs struct {
-	IsGeneralLiked bool
-	LastPlayedAt   *time.Time // nil = never played
-	PlayCount      int        // total play_events
-	SkipCount      int        // play_events with was_skipped=true
+	IsGeneralLiked       bool
+	LastPlayedAt         *time.Time // nil = never played
+	PlayCount            int        // total play_events
+	SkipCount            int        // play_events with was_skipped=true
+	ContextualMatchScore float64    // [0, 1]; 0 when no signal
 }
 
 // ScoringWeights are the operator-tunable knobs. Defaults live in
@@ -24,6 +27,7 @@ type ScoringWeights struct {
 	RecencyWeight   float64
 	SkipPenalty     float64
 	JitterMagnitude float64
+	ContextWeight   float64
 }
 
 // Score computes the weighted-shuffle score per spec §6:
@@ -32,6 +36,7 @@ type ScoringWeights struct {
 //	      + (is_general_liked ? LikeBoost : 0)
 //	      + recency_decay * RecencyWeight
 //	      - skip_ratio * SkipPenalty
+//	      + contextual_match_score * ContextWeight
 //	      + small_random_jitter
 //
 // Higher score = more likely to surface. rng is a function returning a
@@ -44,6 +49,7 @@ func Score(in ScoringInputs, w ScoringWeights, now time.Time, rng func() float64
 	}
 	s += recencyDecay(in.LastPlayedAt, now) * w.RecencyWeight
 	s -= skipRatio(in.PlayCount, in.SkipCount) * w.SkipPenalty
+	s += in.ContextualMatchScore * w.ContextWeight
 	s += (rng()*2 - 1) * w.JitterMagnitude
 	return s
 }

internal/recommendation/score_test.go

@@ -148,3 +148,37 @@ func TestSkipRatio_Half(t *testing.T) {
 		t.Errorf("skipRatio(4,2) = %v, want 0.5", got)
 	}
 }
+
+func TestScore_ContextualMatch_PerfectMatchAtWeight2(t *testing.T) {
+	w := defaultWeights()
+	w.ContextWeight = 2.0
+	in := ScoringInputs{ContextualMatchScore: 1.0}
+	got := Score(in, w, time.Now(), fixedRNG(0.5))
+	// base 1.0 + recency 1.0 (never played) + contextual 2.0 = 4.0
+	want := 4.0
+	if math.Abs(got-want) > 1e-9 {
+		t.Errorf("score = %v, want %v", got, want)
+	}
+}
+
+func TestScore_ContextualMatch_HalfMatchAtWeight2(t *testing.T) {
+	w := defaultWeights()
+	w.ContextWeight = 2.0
+	in := ScoringInputs{ContextualMatchScore: 0.5}
+	got := Score(in, w, time.Now(), fixedRNG(0.5))
+	// base 1.0 + recency 1.0 + contextual 1.0 = 3.0
+	want := 3.0
+	if math.Abs(got-want) > 1e-9 {
+		t.Errorf("score = %v, want %v", got, want)
+	}
+}
+
+func TestScore_ContextualMatch_ZeroNoEffect(t *testing.T) {
+	wWithCtx := defaultWeights()
+	wWithCtx.ContextWeight = 2.0
+	withCtx := Score(ScoringInputs{ContextualMatchScore: 0}, wWithCtx, time.Now(), fixedRNG(0.5))
+	withoutCtx := Score(ScoringInputs{}, defaultWeights(), time.Now(), fixedRNG(0.5))
+	if math.Abs(withCtx-withoutCtx) > 1e-9 {
+		t.Errorf("score-with-zero-ctx = %v, score-without = %v; should be equal", withCtx, withoutCtx)
+	}
+}