feat(recommendation): add ContextualMatchScore (max over non-seed likes)

internal/recommendation/similarity.go

@@ -74,3 +74,28 @@ func setJaccardSlice(a, b []string) float64 {
 	}
 	return float64(intersect) / float64(union)
 }
+
+// ContextualMatchScore returns the maximum Similarity between the current
+// session vector and any non-seed entry in likes. Returns 0 when:
+//   - current.Seed is true (no meaningful current context)
+//   - likes is empty after filtering out Seed=true entries
+//
+// The "max" semantics means a single strong contextual match dominates
+// over many weak ones — we want to surface the track because it was liked
+// in *some* matching context, not because it was vaguely-liked in many.
+func ContextualMatchScore(current SessionVector, likes []SessionVector, w SimilarityWeights) float64 {
+	if current.Seed {
+		return 0.0
+	}
+	best := 0.0
+	for _, l := range likes {
+		if l.Seed {
+			continue
+		}
+		s := Similarity(current, l, w)
+		if s > best {
+			best = s
+		}
+	}
+	return best
+}

internal/recommendation/similarity_test.go

@@ -92,3 +92,66 @@ func TestSimilarity_BagOfCountsCollapsesToSet(t *testing.T) {
 		t.Errorf("set-collapse = %v, want 1.0", got)
 	}
 }
+
+func TestContextualMatchScore_NoLikes_Returns0(t *testing.T) {
+	current := SessionVector{Artists: []string{"a"}, Tags: map[string]int{"rock": 1}}
+	got := ContextualMatchScore(current, nil, DefaultSimilarityWeights)
+	if !approxEq(got, 0.0) {
+		t.Errorf("no likes = %v, want 0.0", got)
+	}
+	got = ContextualMatchScore(current, []SessionVector{}, DefaultSimilarityWeights)
+	if !approxEq(got, 0.0) {
+		t.Errorf("empty likes = %v, want 0.0", got)
+	}
+}
+
+func TestContextualMatchScore_CurrentSeed_Returns0(t *testing.T) {
+	current := SessionVector{Seed: true}
+	likes := []SessionVector{
+		{Artists: []string{"a"}, Tags: map[string]int{"rock": 1}},
+	}
+	got := ContextualMatchScore(current, likes, DefaultSimilarityWeights)
+	if !approxEq(got, 0.0) {
+		t.Errorf("current seed = %v, want 0.0", got)
+	}
+}
+
+func TestContextualMatchScore_AllLikesSeed_Returns0(t *testing.T) {
+	current := SessionVector{Artists: []string{"a"}, Tags: map[string]int{"rock": 1}}
+	likes := []SessionVector{
+		{Seed: true, Artists: []string{"a"}, Tags: map[string]int{"rock": 1}},
+		{Seed: true, Artists: []string{"a"}, Tags: map[string]int{"rock": 1}},
+	}
+	got := ContextualMatchScore(current, likes, DefaultSimilarityWeights)
+	if !approxEq(got, 0.0) {
+		t.Errorf("all-seed likes = %v, want 0.0", got)
+	}
+}
+
+func TestContextualMatchScore_TakesMax(t *testing.T) {
+	// Three likes: full match, partial match, mismatch. Expect full match (1.0).
+	current := SessionVector{Artists: []string{"a1"}, Tags: map[string]int{"rock": 1}}
+	likes := []SessionVector{
+		{Artists: []string{"a1"}, Tags: map[string]int{"rock": 1}},     // 1.0
+		{Artists: []string{"a2"}, Tags: map[string]int{"rock": 1}},     // 0.7
+		{Artists: []string{"a99"}, Tags: map[string]int{"jazz": 1}},    // 0.0
+	}
+	got := ContextualMatchScore(current, likes, DefaultSimilarityWeights)
+	if !approxEq(got, 1.0) {
+		t.Errorf("takes-max = %v, want 1.0", got)
+	}
+}
+
+func TestContextualMatchScore_FiltersSeedThenMaxes(t *testing.T) {
+	// One Seed=true match (would be 1.0 if not filtered) + one partial match.
+	current := SessionVector{Artists: []string{"a1"}, Tags: map[string]int{"rock": 1}}
+	likes := []SessionVector{
+		{Seed: true, Artists: []string{"a1"}, Tags: map[string]int{"rock": 1}},
+		{Artists: []string{"a2"}, Tags: map[string]int{"rock": 1}},
+	}
+	got := ContextualMatchScore(current, likes, DefaultSimilarityWeights)
+	// Seed=true filtered out → only partial match counts → 0.7
+	if !approxEq(got, 0.7) {
+		t.Errorf("filter-then-max = %v, want 0.7", got)
+	}
+}