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
 }