feat(taste): mood taste facet — #1534

Milestone #160 Opt 2b (mood half of the era+mood option). A fourth taste
facet alongside artists + genre tags + eras: signed weights over canonical
mood buckets (melancholic / energetic / chill / …) derived from a track's
enriched folksonomy tags (#1490).

- internal/mood: shared vocabulary — Of(tags) maps folksonomy tags to
  canonical mood buckets (synonyms collapse). Imported by both the taste
  builder and the scorer so a track's mood is derived identically.
- Migration 0047: taste_profile_moods table + taste_tuning.mood_scale
  (DEFAULT 0.5).
- Build side (internal/taste): Config.MoodScale ([0,1] damper, mirrors
  EraScale); accumulate folds each play/like's mood buckets at
  base*MoodScale; persist atomic-replaces the mood rows.
- Scorer (internal/recommendation): TasteProfile gains a mood term
  (own tanh scale + additive 0.12 share, so it never weakens the existing
  signal when a track has no mood tags). Match now takes the candidate's
  mood buckets; loaded per candidate (ListTrackTagsForTracks → mood.Of) in
  the primary similarity loader only — the near-whole-library fallback
  pool passes nil (mood → 0) to avoid a full-library tag scan.
- Tuning lab: mood_scale threaded through recsettings + admin API + web
  card ("Mood weight" row) + Go/web tests.

Coverage is partial (grows with tag enrichment; richer once Last.fm is
keyed), so mood is a supplement — neutral for tracks with no mood tags.

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

internal/recommendation/candidates.go

@@ -8,6 +8,7 @@ import (
 	"github.com/jackc/pgx/v5/pgtype"
 
 	"git.fabledsword.com/bvandeusen/minstrel/internal/db/dbq"
+	"git.fabledsword.com/bvandeusen/minstrel/internal/mood"
 )
 
 // LoadCandidates fetches the candidate pool for radio scoring. Combines
@@ -62,7 +63,10 @@ func LoadCandidates(
 				PlayCount:            int(r.PlayCount),
 				SkipCount:            int(r.SkipCount),
 				ContextualMatchScore: ctxScore,
-				TasteMatchScore:      profile.Match(r.Track.ArtistID, r.Track.Genre, r.ReleaseDate),
+				// Fallback path: mood is scored only in the primary
+				// (similarity) loader — loading per-candidate tags over this
+				// near-whole-library pool isn't worth it (nil moods → 0).
+				TasteMatchScore:      profile.Match(r.Track.ArtistID, r.Track.Genre, r.ReleaseDate, nil),
 				ContextAffinityScore: affinity.Affinity(r.Track.ArtistID),
 			},
 		})
@@ -151,6 +155,15 @@ func LoadCandidatesFromSimilarity(
 		return nil, err
 	}
 
+	trackIDs := make([]pgtype.UUID, 0, len(rows))
+	for _, r := range rows {
+		trackIDs = append(trackIDs, r.Track.ID)
+	}
+	moods, err := loadCandidateMoods(ctx, q, trackIDs)
+	if err != nil {
+		return nil, err
+	}
+
 	out := make([]Candidate, 0, len(rows))
 	for _, r := range rows {
 		var lpt *time.Time
@@ -175,7 +188,8 @@ func LoadCandidatesFromSimilarity(
 				SkipCount:            int(r.SkipCount),
 				ContextualMatchScore: ctxScore,
 				SimilarityScore:      simScore,
-				TasteMatchScore:      profile.Match(r.Track.ArtistID, r.Track.Genre, r.ReleaseDate),
+				TasteMatchScore: profile.Match(
+					r.Track.ArtistID, r.Track.Genre, r.ReleaseDate, moods[r.Track.ID]),
 				ContextAffinityScore: affinity.Affinity(r.Track.ArtistID),
 			},
 		})
@@ -183,6 +197,33 @@ func LoadCandidatesFromSimilarity(
 	return out, nil
 }
 
+// loadCandidateMoods fetches the enriched tags for the given candidate tracks
+// and reduces each to its canonical mood buckets (internal/mood, #1534), so the
+// scorer can apply the mood facet per candidate. Tracks with no mood-word tags
+// are absent from the map (→ no mood signal). Empty input short-circuits.
+func loadCandidateMoods(
+	ctx context.Context, q *dbq.Queries, trackIDs []pgtype.UUID,
+) (map[pgtype.UUID][]string, error) {
+	if len(trackIDs) == 0 {
+		return map[pgtype.UUID][]string{}, nil
+	}
+	rows, err := q.ListTrackTagsForTracks(ctx, trackIDs)
+	if err != nil {
+		return nil, err
+	}
+	tagsByTrack := make(map[pgtype.UUID][]string)
+	for _, r := range rows {
+		tagsByTrack[r.TrackID] = append(tagsByTrack[r.TrackID], r.Tag)
+	}
+	out := make(map[pgtype.UUID][]string, len(tagsByTrack))
+	for id, tags := range tagsByTrack {
+		if m := mood.Of(tags); len(m) > 0 {
+			out[id] = m
+		}
+	}
+	return out, nil
+}
+
 // loadContextualLikesByTrack fetches the user's active contextual_likes in
 // one query and groups them by track_id. Rows whose session_vector fails
 // to unmarshal are skipped with no error (don't poison scoring over one