feat(taste): time-of-day / weekday context conditioning — #1531

Milestone #160 Opt 3 (temporal half). A new additive scoring term that
boosts a candidate when its artist's play history concentrates in the
CURRENT daypart × weekday-type cell, in the user's local timezone.

- Migration 0046: recommendation_weight_profiles.context_time_weight
  (per-profile scoring weight, DEFAULT 1.0).
- Query ListArtistContextPlayCountsForUser: per-artist completed-play
  counts split by the current cell (daypart night[22,5)/morning[5,12)/
  afternoon[12,17)/evening[17,22) × weekday-vs-weekend) via
  started_at AT TIME ZONE users.timezone; 365-day window, skips excluded.
- internal/recommendation/context.go: LoadContextAffinity computes each
  artist's shrunk cell-share minus the user's baseline share, clamped to
  [-1,1]; sparse artists shrink toward baseline (pseudo-count 5), unknown
  artists → 0 (cold-start neutral).
- Score() gains context_affinity_score · ContextTimeWeight; both
  candidate loaders set it per candidate.
- Tuning lab: ContextTimeWeight threaded through recsettings + admin API
  + web card ("Time-of-day weight" row) + Go/web tests. Shipped 1.0 both
  profiles (uniform start, re-bakeable).

Device-class axis deferred to #1551 (needs a client_id → device-class
mapping that doesn't exist yet).

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

internal/api/admin_recommendation_tuning.go

@@ -20,26 +20,28 @@ import (
 // weightsResp is one weight profile on the wire, keyed by the same
 // snake_case field names the PATCH body accepts.
 type weightsResp struct {
-	BaseWeight       float64 `json:"base_weight"`
-	LikeBoost        float64 `json:"like_boost"`
-	RecencyWeight    float64 `json:"recency_weight"`
-	SkipPenalty      float64 `json:"skip_penalty"`
-	JitterMagnitude  float64 `json:"jitter_magnitude"`
-	ContextWeight    float64 `json:"context_weight"`
-	SimilarityWeight float64 `json:"similarity_weight"`
-	TasteWeight      float64 `json:"taste_weight"`
+	BaseWeight        float64 `json:"base_weight"`
+	LikeBoost         float64 `json:"like_boost"`
+	RecencyWeight     float64 `json:"recency_weight"`
+	SkipPenalty       float64 `json:"skip_penalty"`
+	JitterMagnitude   float64 `json:"jitter_magnitude"`
+	ContextWeight     float64 `json:"context_weight"`
+	SimilarityWeight  float64 `json:"similarity_weight"`
+	TasteWeight       float64 `json:"taste_weight"`
+	ContextTimeWeight float64 `json:"context_time_weight"`
 }
 
 func weightsRespFrom(w recommendation.ScoringWeights) weightsResp {
 	return weightsResp{
-		BaseWeight:       w.BaseWeight,
-		LikeBoost:        w.LikeBoost,
-		RecencyWeight:    w.RecencyWeight,
-		SkipPenalty:      w.SkipPenalty,
-		JitterMagnitude:  w.JitterMagnitude,
-		ContextWeight:    w.ContextWeight,
-		SimilarityWeight: w.SimilarityWeight,
-		TasteWeight:      w.TasteWeight,
+		BaseWeight:        w.BaseWeight,
+		LikeBoost:         w.LikeBoost,
+		RecencyWeight:     w.RecencyWeight,
+		SkipPenalty:       w.SkipPenalty,
+		JitterMagnitude:   w.JitterMagnitude,
+		ContextWeight:     w.ContextWeight,
+		SimilarityWeight:  w.SimilarityWeight,
+		TasteWeight:       w.TasteWeight,
+		ContextTimeWeight: w.ContextTimeWeight,
 	}
 }
 

internal/db/dbq/models.go

@@ -446,16 +446,17 @@ type RecommendationTuningAudit struct {
 }
 
 type RecommendationWeightProfile struct {
-	Profile          string
-	BaseWeight       float64
-	LikeBoost        float64
-	RecencyWeight    float64
-	SkipPenalty      float64
-	JitterMagnitude  float64
-	ContextWeight    float64
-	SimilarityWeight float64
-	TasteWeight      float64
-	UpdatedAt        pgtype.Timestamptz
+	Profile           string
+	BaseWeight        float64
+	LikeBoost         float64
+	RecencyWeight     float64
+	SkipPenalty       float64
+	JitterMagnitude   float64
+	ContextWeight     float64
+	SimilarityWeight  float64
+	TasteWeight       float64
+	UpdatedAt         pgtype.Timestamptz
+	ContextTimeWeight float64
 }
 
 type RegistrationSetting struct {

internal/db/dbq/recommendation.sql.go

@@ -11,6 +11,83 @@ import (
 	"github.com/jackc/pgx/v5/pgtype"
 )
 
+const listArtistContextPlayCountsForUser = `-- name: ListArtistContextPlayCountsForUser :many
+WITH tz AS (
+    SELECT COALESCE(NULLIF(u.timezone, ''), 'UTC') AS zone
+    FROM users u WHERE u.id = $1
+),
+now_cell AS (
+    SELECT
+        CASE
+            WHEN EXTRACT(hour FROM now() AT TIME ZONE tz.zone) < 5  THEN 3
+            WHEN EXTRACT(hour FROM now() AT TIME ZONE tz.zone) < 12 THEN 0
+            WHEN EXTRACT(hour FROM now() AT TIME ZONE tz.zone) < 17 THEN 1
+            WHEN EXTRACT(hour FROM now() AT TIME ZONE tz.zone) < 22 THEN 2
+            ELSE 3
+        END AS daypart,
+        (EXTRACT(isodow FROM now() AT TIME ZONE tz.zone) >= 6) AS is_weekend
+    FROM tz
+),
+plays AS (
+    SELECT t.artist_id,
+           CASE
+               WHEN EXTRACT(hour FROM pe.started_at AT TIME ZONE tz.zone) < 5  THEN 3
+               WHEN EXTRACT(hour FROM pe.started_at AT TIME ZONE tz.zone) < 12 THEN 0
+               WHEN EXTRACT(hour FROM pe.started_at AT TIME ZONE tz.zone) < 17 THEN 1
+               WHEN EXTRACT(hour FROM pe.started_at AT TIME ZONE tz.zone) < 22 THEN 2
+               ELSE 3
+           END AS daypart,
+           (EXTRACT(isodow FROM pe.started_at AT TIME ZONE tz.zone) >= 6) AS is_weekend
+    FROM play_events pe
+    JOIN tracks t ON t.id = pe.track_id
+    CROSS JOIN tz
+    WHERE pe.user_id = $1
+      AND pe.was_skipped = false
+      AND pe.started_at > now() - interval '365 days'
+)
+SELECT p.artist_id,
+       count(*) AS total_plays,
+       count(*) FILTER (
+           WHERE p.daypart = (SELECT daypart FROM now_cell)
+             AND p.is_weekend = (SELECT is_weekend FROM now_cell)
+       ) AS cell_plays
+FROM plays p
+GROUP BY p.artist_id
+`
+
+type ListArtistContextPlayCountsForUserRow struct {
+	ArtistID   pgtype.UUID
+	TotalPlays int64
+	CellPlays  int64
+}
+
+// Per-artist completed-play counts split by whether each play falls in the
+// CURRENT daypart × weekday-type cell, in the user's local timezone (#1531).
+// Feeds the context-affinity scoring term: an artist whose plays concentrate
+// in the current cell (vs the user's overall baseline, computed Go-side from
+// these rows) gets boosted right now. Skips excluded; a 365-day window bounds
+// cost. Daypart buckets: night [22,5) morning [5,12) afternoon [12,17)
+// evening [17,22). Weekend = ISO days 6–7 (Sat/Sun).
+func (q *Queries) ListArtistContextPlayCountsForUser(ctx context.Context, id pgtype.UUID) ([]ListArtistContextPlayCountsForUserRow, error) {
+	rows, err := q.db.Query(ctx, listArtistContextPlayCountsForUser, id)
+	if err != nil {
+		return nil, err
+	}
+	defer rows.Close()
+	var items []ListArtistContextPlayCountsForUserRow
+	for rows.Next() {
+		var i ListArtistContextPlayCountsForUserRow
+		if err := rows.Scan(&i.ArtistID, &i.TotalPlays, &i.CellPlays); err != nil {
+			return nil, err
+		}
+		items = append(items, i)
+	}
+	if err := rows.Err(); err != nil {
+		return nil, err
+	}
+	return items, nil
+}
+
 const listLastPlayedArtistsForUser = `-- name: ListLastPlayedArtistsForUser :many
 WITH user_plays AS (
     SELECT t.artist_id, max(pe.started_at) AS last_started

internal/db/dbq/recommendation_tuning.sql.go

@@ -82,7 +82,7 @@ func (q *Queries) ListTuningAudit(ctx context.Context, limit int32) ([]Recommend
 }
 
 const listWeightProfiles = `-- name: ListWeightProfiles :many
-SELECT profile, base_weight, like_boost, recency_weight, skip_penalty, jitter_magnitude, context_weight, similarity_weight, taste_weight, updated_at FROM recommendation_weight_profiles ORDER BY profile
+SELECT profile, base_weight, like_boost, recency_weight, skip_penalty, jitter_magnitude, context_weight, similarity_weight, taste_weight, updated_at, context_time_weight FROM recommendation_weight_profiles ORDER BY profile
 `
 
 func (q *Queries) ListWeightProfiles(ctx context.Context) ([]RecommendationWeightProfile, error) {
@@ -105,6 +105,7 @@ func (q *Queries) ListWeightProfiles(ctx context.Context) ([]RecommendationWeigh
 			&i.SimilarityWeight,
 			&i.TasteWeight,
 			&i.UpdatedAt,
+			&i.ContextTimeWeight,
 		); err != nil {
 			return nil, err
 		}
@@ -163,29 +164,31 @@ func (q *Queries) UpdateTasteTuning(ctx context.Context, arg UpdateTasteTuningPa
 
 const updateWeightProfile = `-- name: UpdateWeightProfile :one
 UPDATE recommendation_weight_profiles
-   SET base_weight       = $2,
-       like_boost        = $3,
-       recency_weight    = $4,
-       skip_penalty      = $5,
-       jitter_magnitude  = $6,
-       context_weight    = $7,
-       similarity_weight = $8,
-       taste_weight      = $9,
-       updated_at        = now()
+   SET base_weight         = $2,
+       like_boost          = $3,
+       recency_weight      = $4,
+       skip_penalty        = $5,
+       jitter_magnitude    = $6,
+       context_weight      = $7,
+       similarity_weight   = $8,
+       taste_weight        = $9,
+       context_time_weight = $10,
+       updated_at          = now()
  WHERE profile = $1
-RETURNING profile, base_weight, like_boost, recency_weight, skip_penalty, jitter_magnitude, context_weight, similarity_weight, taste_weight, updated_at
+RETURNING profile, base_weight, like_boost, recency_weight, skip_penalty, jitter_magnitude, context_weight, similarity_weight, taste_weight, updated_at, context_time_weight
 `
 
 type UpdateWeightProfileParams struct {
-	Profile          string
-	BaseWeight       float64
-	LikeBoost        float64
-	RecencyWeight    float64
-	SkipPenalty      float64
-	JitterMagnitude  float64
-	ContextWeight    float64
-	SimilarityWeight float64
-	TasteWeight      float64
+	Profile           string
+	BaseWeight        float64
+	LikeBoost         float64
+	RecencyWeight     float64
+	SkipPenalty       float64
+	JitterMagnitude   float64
+	ContextWeight     float64
+	SimilarityWeight  float64
+	TasteWeight       float64
+	ContextTimeWeight float64
 }
 
 func (q *Queries) UpdateWeightProfile(ctx context.Context, arg UpdateWeightProfileParams) (RecommendationWeightProfile, error) {
@@ -199,6 +202,7 @@ func (q *Queries) UpdateWeightProfile(ctx context.Context, arg UpdateWeightProfi
 		arg.ContextWeight,
 		arg.SimilarityWeight,
 		arg.TasteWeight,
+		arg.ContextTimeWeight,
 	)
 	var i RecommendationWeightProfile
 	err := row.Scan(
@@ -212,6 +216,7 @@ func (q *Queries) UpdateWeightProfile(ctx context.Context, arg UpdateWeightProfi
 		&i.SimilarityWeight,
 		&i.TasteWeight,
 		&i.UpdatedAt,
+		&i.ContextTimeWeight,
 	)
 	return i, err
 }
@@ -249,21 +254,23 @@ const upsertWeightProfileDefaults = `-- name: UpsertWeightProfileDefaults :exec
 
 INSERT INTO recommendation_weight_profiles (
     profile, base_weight, like_boost, recency_weight, skip_penalty,
-    jitter_magnitude, context_weight, similarity_weight, taste_weight
-) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9)
+    jitter_magnitude, context_weight, similarity_weight, taste_weight,
+    context_time_weight
+) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10)
 ON CONFLICT (profile) DO NOTHING
 `
 
 type UpsertWeightProfileDefaultsParams struct {
-	Profile          string
-	BaseWeight       float64
-	LikeBoost        float64
-	RecencyWeight    float64
-	SkipPenalty      float64
-	JitterMagnitude  float64
-	ContextWeight    float64
-	SimilarityWeight float64
-	TasteWeight      float64
+	Profile           string
+	BaseWeight        float64
+	LikeBoost         float64
+	RecencyWeight     float64
+	SkipPenalty       float64
+	JitterMagnitude   float64
+	ContextWeight     float64
+	SimilarityWeight  float64
+	TasteWeight       float64
+	ContextTimeWeight float64
 }
 
 // Recommendation tuning lab queries (#1250). Seeding happens via the
@@ -281,6 +288,7 @@ func (q *Queries) UpsertWeightProfileDefaults(ctx context.Context, arg UpsertWei
 		arg.ContextWeight,
 		arg.SimilarityWeight,
 		arg.TasteWeight,
+		arg.ContextTimeWeight,
 	)
 	return err
 }

internal/db/migrations/0046_context_time_weight.down.sql

@@ -0,0 +1 @@
+ALTER TABLE recommendation_weight_profiles DROP COLUMN IF EXISTS context_time_weight;

internal/db/migrations/0046_context_time_weight.up.sql

@@ -0,0 +1,12 @@
+-- 0046_context_time_weight.up.sql — time-of-day/weekday context conditioning
+-- (#1531, milestone #160 Opt 3). Adds a per-profile scoring weight for the new
+-- context-affinity term: how strongly a candidate is boosted when its artist's
+-- play history concentrates in the current daypart × weekday-type cell (in the
+-- user's local timezone). Mirrors the other ScoringWeights columns.
+--
+-- DEFAULT 1.0 backfills both existing profile rows to a modest on-value; the
+-- Go ShippedRadioWeights/ShippedDailyMixWeights carry the same 1.0 so fresh
+-- installs seed identically. Reconcile only seeds MISSING rows (ON CONFLICT DO
+-- NOTHING), so existing rows rely on this DEFAULT until an operator resets.
+ALTER TABLE recommendation_weight_profiles
+    ADD COLUMN context_time_weight double precision NOT NULL DEFAULT 1.0;

internal/db/queries/recommendation.sql

@@ -173,6 +173,56 @@ GROUP BY t.id, t.title, t.album_id, t.artist_id, t.duration_ms, t.file_path,
          l.user_id, pe.last_played_at, pe.play_count, pe.skip_count,
          al.release_date;
 
+-- name: ListArtistContextPlayCountsForUser :many
+-- Per-artist completed-play counts split by whether each play falls in the
+-- CURRENT daypart × weekday-type cell, in the user's local timezone (#1531).
+-- Feeds the context-affinity scoring term: an artist whose plays concentrate
+-- in the current cell (vs the user's overall baseline, computed Go-side from
+-- these rows) gets boosted right now. Skips excluded; a 365-day window bounds
+-- cost. Daypart buckets: night [22,5) morning [5,12) afternoon [12,17)
+-- evening [17,22). Weekend = ISO days 6–7 (Sat/Sun).
+WITH tz AS (
+    SELECT COALESCE(NULLIF(u.timezone, ''), 'UTC') AS zone
+    FROM users u WHERE u.id = $1
+),
+now_cell AS (
+    SELECT
+        CASE
+            WHEN EXTRACT(hour FROM now() AT TIME ZONE tz.zone) < 5  THEN 3
+            WHEN EXTRACT(hour FROM now() AT TIME ZONE tz.zone) < 12 THEN 0
+            WHEN EXTRACT(hour FROM now() AT TIME ZONE tz.zone) < 17 THEN 1
+            WHEN EXTRACT(hour FROM now() AT TIME ZONE tz.zone) < 22 THEN 2
+            ELSE 3
+        END AS daypart,
+        (EXTRACT(isodow FROM now() AT TIME ZONE tz.zone) >= 6) AS is_weekend
+    FROM tz
+),
+plays AS (
+    SELECT t.artist_id,
+           CASE
+               WHEN EXTRACT(hour FROM pe.started_at AT TIME ZONE tz.zone) < 5  THEN 3
+               WHEN EXTRACT(hour FROM pe.started_at AT TIME ZONE tz.zone) < 12 THEN 0
+               WHEN EXTRACT(hour FROM pe.started_at AT TIME ZONE tz.zone) < 17 THEN 1
+               WHEN EXTRACT(hour FROM pe.started_at AT TIME ZONE tz.zone) < 22 THEN 2
+               ELSE 3
+           END AS daypart,
+           (EXTRACT(isodow FROM pe.started_at AT TIME ZONE tz.zone) >= 6) AS is_weekend
+    FROM play_events pe
+    JOIN tracks t ON t.id = pe.track_id
+    CROSS JOIN tz
+    WHERE pe.user_id = $1
+      AND pe.was_skipped = false
+      AND pe.started_at > now() - interval '365 days'
+)
+SELECT p.artist_id,
+       count(*) AS total_plays,
+       count(*) FILTER (
+           WHERE p.daypart = (SELECT daypart FROM now_cell)
+             AND p.is_weekend = (SELECT is_weekend FROM now_cell)
+       ) AS cell_plays
+FROM plays p
+GROUP BY p.artist_id;
+
 -- name: SuggestArtistsForUser :many
 -- M5c: per-user artist suggestions ranked by signal x similarity. The
 -- seeds CTE collects the user's likes (x5) plus recency-decayed plays

internal/db/queries/recommendation_tuning.sql

@@ -6,8 +6,9 @@
 -- doesn't exist yet. Never overwrites operator-tuned values.
 INSERT INTO recommendation_weight_profiles (
     profile, base_weight, like_boost, recency_weight, skip_penalty,
-    jitter_magnitude, context_weight, similarity_weight, taste_weight
-) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9)
+    jitter_magnitude, context_weight, similarity_weight, taste_weight,
+    context_time_weight
+) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10)
 ON CONFLICT (profile) DO NOTHING;
 
 -- name: ListWeightProfiles :many
@@ -15,15 +16,16 @@ SELECT * FROM recommendation_weight_profiles ORDER BY profile;
 
 -- name: UpdateWeightProfile :one
 UPDATE recommendation_weight_profiles
-   SET base_weight       = $2,
-       like_boost        = $3,
-       recency_weight    = $4,
-       skip_penalty      = $5,
-       jitter_magnitude  = $6,
-       context_weight    = $7,
-       similarity_weight = $8,
-       taste_weight      = $9,
-       updated_at        = now()
+   SET base_weight         = $2,
+       like_boost          = $3,
+       recency_weight      = $4,
+       skip_penalty        = $5,
+       jitter_magnitude    = $6,
+       context_weight      = $7,
+       similarity_weight   = $8,
+       taste_weight        = $9,
+       context_time_weight = $10,
+       updated_at          = now()
  WHERE profile = $1
 RETURNING *;
 

internal/playlists/system.go

@@ -215,6 +215,9 @@ var (
 		// [-1,+1], so 1.5 makes a strong taste fit comparable to a like boost
 		// while passive avoidance (negative) gently demotes.
 		TasteWeight: 1.5,
+		// Time-of-day/weekday context affinity (#1531), in [-1,+1]. Starts
+		// uniform with radio pending trend data.
+		ContextTimeWeight: 1.0,
 	}
 	systemTasteConfig = taste.DefaultConfig()
 )

internal/recommendation/candidates.go

@@ -41,6 +41,11 @@ func LoadCandidates(
 		return nil, err
 	}
 
+	affinity, err := LoadContextAffinity(ctx, q, userID)
+	if err != nil {
+		return nil, err
+	}
+
 	out := make([]Candidate, 0, len(rows))
 	for _, r := range rows {
 		var lpt *time.Time
@@ -58,6 +63,7 @@ func LoadCandidates(
 				SkipCount:            int(r.SkipCount),
 				ContextualMatchScore: ctxScore,
 				TasteMatchScore:      profile.Match(r.Track.ArtistID, r.Track.Genre, r.ReleaseDate),
+				ContextAffinityScore: affinity.Affinity(r.Track.ArtistID),
 			},
 		})
 	}
@@ -134,6 +140,11 @@ func LoadCandidatesFromSimilarity(
 		return nil, err
 	}
 
+	affinity, err := LoadContextAffinity(ctx, q, userID)
+	if err != nil {
+		return nil, err
+	}
+
 	out := make([]Candidate, 0, len(rows))
 	for _, r := range rows {
 		var lpt *time.Time
@@ -159,6 +170,7 @@ func LoadCandidatesFromSimilarity(
 				ContextualMatchScore: ctxScore,
 				SimilarityScore:      simScore,
 				TasteMatchScore:      profile.Match(r.Track.ArtistID, r.Track.Genre, r.ReleaseDate),
+				ContextAffinityScore: affinity.Affinity(r.Track.ArtistID),
 			},
 		})
 	}

internal/recommendation/context.go

@@ -0,0 +1,70 @@
+package recommendation
+
+import (
+	"context"
+
+	"github.com/jackc/pgx/v5/pgtype"
+
+	"git.fabledsword.com/bvandeusen/minstrel/internal/db/dbq"
+)
+
+// contextAffinityShrinkage is the pseudo-count that pulls a low-play artist's
+// cell-share toward the user's baseline, so an artist with one or two plays
+// can't swing its affinity to ±1 on noise. At k plays the estimate sits
+// halfway between the raw cell-share and the baseline.
+const contextAffinityShrinkage = 5.0
+
+// ContextAffinity is the read-side map of per-artist time-of-day/weekday
+// affinity for the CURRENT context (#1531): artist_id → score in [-1, +1].
+// Absent artists (no play history) score 0, so cold-start candidates stay
+// neutral. The zero value is a valid all-neutral affinity.
+type ContextAffinity struct {
+	byArtist map[pgtype.UUID]float64
+}
+
+// Affinity returns the artist's current-context affinity, or 0 if unknown.
+func (c ContextAffinity) Affinity(artistID pgtype.UUID) float64 {
+	return c.byArtist[artistID]
+}
+
+// LoadContextAffinity computes each artist's affinity for the user's CURRENT
+// daypart × weekday cell. For every artist with completed plays in the window
+// it compares the share of that artist's plays that fall in the current cell
+// against the user's overall baseline share, shrinking sparse artists toward
+// the baseline. Returns an empty (all-neutral) affinity when the user has no
+// plays.
+func LoadContextAffinity(
+	ctx context.Context, q *dbq.Queries, userID pgtype.UUID,
+) (ContextAffinity, error) {
+	rows, err := q.ListArtistContextPlayCountsForUser(ctx, userID)
+	if err != nil {
+		return ContextAffinity{}, err
+	}
+	var totalPlays, cellPlays int64
+	for _, r := range rows {
+		totalPlays += r.TotalPlays
+		cellPlays += r.CellPlays
+	}
+	out := ContextAffinity{byArtist: make(map[pgtype.UUID]float64, len(rows))}
+	if totalPlays == 0 {
+		return out, nil
+	}
+	baseline := float64(cellPlays) / float64(totalPlays)
+	for _, r := range rows {
+		out.byArtist[r.ArtistID] = contextAffinity(
+			float64(r.CellPlays), float64(r.TotalPlays), baseline, contextAffinityShrinkage)
+	}
+	return out, nil
+}
+
+// contextAffinity returns an artist's shrunk cell-share minus the user's
+// baseline share, clamped to [-1, 1]. The shrinkage pseudo-count k pulls
+// low-play artists toward the baseline (→ 0 affinity) so noise can't dominate;
+// a heavily-played artist keeps close to its raw over/under-representation.
+func contextAffinity(cellPlays, totalPlays, baseline, k float64) float64 {
+	if totalPlays == 0 {
+		return 0
+	}
+	shrunk := (cellPlays + baseline*k) / (totalPlays + k)
+	return clampUnit(shrunk - baseline)
+}

internal/recommendation/context_test.go

@@ -0,0 +1,57 @@
+package recommendation
+
+import (
+	"testing"
+	"time"
+)
+
+func TestContextAffinity(t *testing.T) {
+	const baseline = 0.4 // 40% of the user's plays fall in the current cell
+	const k = contextAffinityShrinkage
+
+	// Heavy history, over-represented in the current cell → positive.
+	if a := contextAffinity(80, 100, baseline, k); a <= 0 {
+		t.Errorf("over-represented artist affinity = %.3f, want positive", a)
+	}
+	// Heavy history, under-represented → negative.
+	if a := contextAffinity(10, 100, baseline, k); a >= 0 {
+		t.Errorf("under-represented artist affinity = %.3f, want negative", a)
+	}
+	// A sparse artist (1/1) shrinks toward the baseline, so its affinity is
+	// smaller than a heavily-played artist with the same raw cell-share.
+	sparse := contextAffinity(1, 1, baseline, k)
+	heavy := contextAffinity(100, 100, baseline, k)
+	if sparse >= heavy {
+		t.Errorf("sparse (%.3f) should shrink below heavy (%.3f)", sparse, heavy)
+	}
+	// No plays → neutral.
+	if a := contextAffinity(0, 0, baseline, k); a != 0 {
+		t.Errorf("no plays affinity = %.3f, want 0", a)
+	}
+	// Result stays within [-1, 1].
+	for _, tc := range [][2]float64{{100, 100}, {0, 100}, {50, 50}} {
+		a := contextAffinity(tc[0], tc[1], baseline, k)
+		if a < -1 || a > 1 {
+			t.Errorf("affinity out of [-1,1]: %.3f", a)
+		}
+	}
+}
+
+func TestScore_ContextTermAddsAndSubtracts(t *testing.T) {
+	now := time.Now()
+	zeroJitter := func() float64 { return 0.5 } // (0.5*2-1)=0 with any magnitude
+	w := ScoringWeights{ContextTimeWeight: 2.0} // all other weights 0
+
+	pos := Score(ScoringInputs{ContextAffinityScore: 1.0}, w, now, zeroJitter)
+	if !almostEq(pos, 2.0) {
+		t.Errorf("positive context affinity: Score = %.3f, want 2.0", pos)
+	}
+	neg := Score(ScoringInputs{ContextAffinityScore: -1.0}, w, now, zeroJitter)
+	if !almostEq(neg, -2.0) {
+		t.Errorf("negative context affinity: Score = %.3f, want -2.0 (demotes)", neg)
+	}
+	off := Score(ScoringInputs{ContextAffinityScore: 1.0}, ScoringWeights{}, now, zeroJitter)
+	if !almostEq(off, 0.0) {
+		t.Errorf("ContextTimeWeight 0: Score = %.3f, want 0 (no effect)", off)
+	}
+}

internal/recommendation/score.go

@@ -24,19 +24,26 @@ type ScoringInputs struct {
 	// user's taste, negative reflects passive avoidance, 0 when there's no
 	// profile signal (cold start / artist+tags absent from the profile).
 	TasteMatchScore float64
+	// ContextAffinityScore is the candidate artist's time-of-day/weekday
+	// affinity for the CURRENT context (#1531), in [-1, +1]: positive when the
+	// artist's plays concentrate in the current daypart × weekday-type cell
+	// more than the user's baseline, negative when under-represented, 0 when
+	// there's no history (cold-start neutral).
+	ContextAffinityScore float64
 }
 
 // ScoringWeights are the operator-tunable knobs. Defaults live in
 // config.RecommendationConfig and are propagated here per request.
 type ScoringWeights struct {
-	BaseWeight       float64
-	LikeBoost        float64
-	RecencyWeight    float64
-	SkipPenalty      float64
-	JitterMagnitude  float64
-	ContextWeight    float64
-	SimilarityWeight float64
-	TasteWeight      float64
+	BaseWeight        float64
+	LikeBoost         float64
+	RecencyWeight     float64
+	SkipPenalty       float64
+	JitterMagnitude   float64
+	ContextWeight     float64
+	SimilarityWeight  float64
+	TasteWeight       float64
+	ContextTimeWeight float64
 }
 
 // Score computes the weighted-shuffle score per spec §6:
@@ -48,6 +55,7 @@ type ScoringWeights struct {
 //	      + contextual_match_score * ContextWeight
 //	      + similarity_score * SimilarityWeight
 //	      + taste_match_score * TasteWeight
+//	      + context_affinity_score * ContextTimeWeight
 //	      + small_random_jitter
 //
 // Higher score = more likely to surface. rng is a function returning a
@@ -63,6 +71,7 @@ func Score(in ScoringInputs, w ScoringWeights, now time.Time, rng func() float64
 	s += in.ContextualMatchScore * w.ContextWeight
 	s += in.SimilarityScore * w.SimilarityWeight
 	s += in.TasteMatchScore * w.TasteWeight
+	s += in.ContextAffinityScore * w.ContextTimeWeight
 	s += (rng()*2 - 1) * w.JitterMagnitude
 	return s
 }

internal/recsettings/patch.go

@@ -67,6 +67,10 @@ var weightFields = map[string]weightField{
 		get: func(w recommendation.ScoringWeights) float64 { return w.TasteWeight },
 		set: func(w *recommendation.ScoringWeights, v float64) { w.TasteWeight = v },
 	},
+	"context_time_weight": {
+		get: func(w recommendation.ScoringWeights) float64 { return w.ContextTimeWeight },
+		set: func(w *recommendation.ScoringWeights, v float64) { w.ContextTimeWeight = v },
+	},
 }
 
 // applyWeightPatch validates and applies a partial update, returning

internal/recsettings/service.go

@@ -54,16 +54,19 @@ type TasteTuning struct {
 // here from config.RecommendationConfig — YAML is bootstrap-only,
 // rule: config in UI). Radio is seed-directed (the user picked a
 // direction), so taste is a lighter nudge than in the daily mixes.
+// ContextTimeWeight starts uniform (1.0) across both profiles pending
+// trend data (#1531); split them once the metrics view justifies it.
 func ShippedRadioWeights() recommendation.ScoringWeights {
 	return recommendation.ScoringWeights{
-		BaseWeight:       1.0,
-		LikeBoost:        2.0,
-		RecencyWeight:    1.0,
-		SkipPenalty:      1.0,
-		JitterMagnitude:  0.1,
-		ContextWeight:    2.0,
-		SimilarityWeight: 2.0,
-		TasteWeight:      1.0,
+		BaseWeight:        1.0,
+		LikeBoost:         2.0,
+		RecencyWeight:     1.0,
+		SkipPenalty:       1.0,
+		JitterMagnitude:   0.1,
+		ContextWeight:     2.0,
+		SimilarityWeight:  2.0,
+		TasteWeight:       1.0,
+		ContextTimeWeight: 1.0,
 	}
 }
 
@@ -71,14 +74,15 @@ func ShippedRadioWeights() recommendation.ScoringWeights {
 // Must stay in sync with the pre-push literal in playlists/system.go.
 func ShippedDailyMixWeights() recommendation.ScoringWeights {
 	return recommendation.ScoringWeights{
-		BaseWeight:       1.0,
-		LikeBoost:        2.0,
-		RecencyWeight:    1.0,
-		SkipPenalty:      2.0,
-		JitterMagnitude:  0.1,
-		ContextWeight:    0.5,
-		SimilarityWeight: 1.5,
-		TasteWeight:      1.5,
+		BaseWeight:        1.0,
+		LikeBoost:         2.0,
+		RecencyWeight:     1.0,
+		SkipPenalty:       2.0,
+		JitterMagnitude:   0.1,
+		ContextWeight:     0.5,
+		SimilarityWeight:  1.5,
+		TasteWeight:       1.5,
+		ContextTimeWeight: 1.0,
 	}
 }
 
@@ -348,41 +352,44 @@ func (s *Service) audit(
 
 func upsertParams(profile string, w recommendation.ScoringWeights) dbq.UpsertWeightProfileDefaultsParams {
 	return dbq.UpsertWeightProfileDefaultsParams{
-		Profile:          profile,
-		BaseWeight:       w.BaseWeight,
-		LikeBoost:        w.LikeBoost,
-		RecencyWeight:    w.RecencyWeight,
-		SkipPenalty:      w.SkipPenalty,
-		JitterMagnitude:  w.JitterMagnitude,
-		ContextWeight:    w.ContextWeight,
-		SimilarityWeight: w.SimilarityWeight,
-		TasteWeight:      w.TasteWeight,
+		Profile:           profile,
+		BaseWeight:        w.BaseWeight,
+		LikeBoost:         w.LikeBoost,
+		RecencyWeight:     w.RecencyWeight,
+		SkipPenalty:       w.SkipPenalty,
+		JitterMagnitude:   w.JitterMagnitude,
+		ContextWeight:     w.ContextWeight,
+		SimilarityWeight:  w.SimilarityWeight,
+		TasteWeight:       w.TasteWeight,
+		ContextTimeWeight: w.ContextTimeWeight,
 	}
 }
 
 func updateParams(profile string, w recommendation.ScoringWeights) dbq.UpdateWeightProfileParams {
 	return dbq.UpdateWeightProfileParams{
-		Profile:          profile,
-		BaseWeight:       w.BaseWeight,
-		LikeBoost:        w.LikeBoost,
-		RecencyWeight:    w.RecencyWeight,
-		SkipPenalty:      w.SkipPenalty,
-		JitterMagnitude:  w.JitterMagnitude,
-		ContextWeight:    w.ContextWeight,
-		SimilarityWeight: w.SimilarityWeight,
-		TasteWeight:      w.TasteWeight,
+		Profile:           profile,
+		BaseWeight:        w.BaseWeight,
+		LikeBoost:         w.LikeBoost,
+		RecencyWeight:     w.RecencyWeight,
+		SkipPenalty:       w.SkipPenalty,
+		JitterMagnitude:   w.JitterMagnitude,
+		ContextWeight:     w.ContextWeight,
+		SimilarityWeight:  w.SimilarityWeight,
+		TasteWeight:       w.TasteWeight,
+		ContextTimeWeight: w.ContextTimeWeight,
 	}
 }
 
 func weightsFromRow(r dbq.RecommendationWeightProfile) recommendation.ScoringWeights {
 	return recommendation.ScoringWeights{
-		BaseWeight:       r.BaseWeight,
-		LikeBoost:        r.LikeBoost,
-		RecencyWeight:    r.RecencyWeight,
-		SkipPenalty:      r.SkipPenalty,
-		JitterMagnitude:  r.JitterMagnitude,
-		ContextWeight:    r.ContextWeight,
-		SimilarityWeight: r.SimilarityWeight,
-		TasteWeight:      r.TasteWeight,
+		BaseWeight:        r.BaseWeight,
+		LikeBoost:         r.LikeBoost,
+		RecencyWeight:     r.RecencyWeight,
+		SkipPenalty:       r.SkipPenalty,
+		JitterMagnitude:   r.JitterMagnitude,
+		ContextWeight:     r.ContextWeight,
+		SimilarityWeight:  r.SimilarityWeight,
+		TasteWeight:       r.TasteWeight,
+		ContextTimeWeight: r.ContextTimeWeight,
 	}
 }