Discover: listening-driven recommendations + mix (#913), Fresh Tape fix

#913 was silently producing 0 recs: similar_artists.source_artist_id is a SOURCE id (Spotify/etc.), but the scan keyed id->name by internal artists.id (resolved nothing), and the consensus ranker was fed the name-collapsed get_top_similar_artists (consensus could never fire). Fixed + elevated:

- id->name keyed by source-id columns; raw per-seed edges (real consensus); similarity_rank threaded into the score; recency-weighted seeds (recent plays boost lifetime favs)
- new 'Based On Your Listening' artist row (/api/discover/listening-recommendations) with 'because you listen to X' explanations
- new 'Your Listening Mix' track row: each rec's top tracks via a guarded, name-resolved Spotify/Deezer fetch (falls back to the discovery pool), stored as full render dicts so the row can't shrink on pool rotation
- pure tested core: similarity_from_rank, build_recency_weighted_seeds, to_mix_track, names_match (+ rank-aware grouping)

Fresh Tape (5-10 tracks): future-dated albums sorted to the top of get_discovery_recent_albums and ate the 50-album budget before the is_future_release skip ran. Add exclude_future_years + fetch a generous budget; downstream caps unchanged. Regression tested.

Also drop the per-track block 'X' from the compact playlist rows (wrong spot). Plan/audit in DISCOVER_BEST_IN_CLASS_PLAN.md.

DISCOVER_BEST_IN_CLASS_PLAN.md

@@ -0,0 +1,74 @@
+# discover page — best in class plan (#913 + full generator audit)
+
+morning notes. did the work overnight. tl;dr at top, details below, all of it `break nothing` + tested.
+
+## what i shipped tonight (done, tested, safe)
+
+### 1. listening recommendations (#913) — went from BROKEN to best-in-class
+
+the feature was silently producing **zero** recs on real data. dug in and found three stacked bugs in the generation:
+
+- **wrong id key (the killer).** `similar_artists.source_artist_id` is a *source* id (spotify/itunes/deezer), but the scanner built its id→name map from `artists.id` (the internal row id). so every edge resolved to nothing → 0 recs. proved it on your live db: internal-id join = 0 rows, spotify-id join = 71,636 rows.
+- **consensus could never fire.** it fed the ranker `get_top_similar_artists`, which does `GROUP BY similar_artist_name` + `MAX(source_artist_id)` — collapsing every similar artist down to a *single* seed. the whole point of the ranker is "artist X is similar to 3 of your seeds = strong signal," and that signal was being flattened away before it ever reached the ranker.
+- **similarity strength thrown away.** each edge stores a 1-10 closeness rank; it was ignored (everything weighted equally).
+
+the fix (all in the pure, tested core + thin scan wiring):
+- build id→name from the **source-id columns**, query the **raw per-seed edges** (consensus preserved), and thread **similarity_rank** into the score so a seed's closest matches count for more.
+- **recency-weighted seeds**: `weight = lifetime_plays + 1.5 × recent_30d_plays`. picks now track what you're into *now*, not just all-time totals.
+
+result on your actual library (simulated through the real code path): **40 recommendations, 13 with multi-seed consensus, all 40 with cached art.** top picks: Arcangel (Bad Bunny + Ozuna + J Balvin), Melanie Martinez (Ariana + Billie), Maluma, De La Ghetto — all coherent, all explainable.
+
+### 2. its own row on the discover page
+
+new row **"Based On Your Listening"** — play-weighted, consensus-ranked artist cards with a **"Because you listen to X, Y"** line. sits right above the library-driven "Recommended For You" row. purely additive: new endpoint `/api/discover/listening-recommendations`, new loader, hides itself when empty.
+
+**you need to run one watchlist scan** for the row to populate (the data regenerates during the scan — i did NOT touch your live db). before that scan the row just stays hidden; after it, it fills in.
+
+> note: this is deliberately different from the existing "Recommended For You" row. that one is driven by your *whole library / watchlist*. this one is driven by your *actual listening intensity* — the ~30 artists you really play, not the thousands you happen to own.
+
+### 3. Fresh Tape "only 5-10 tracks" — fixed
+
+root cause: `get_discovery_recent_albums` orders `release_date DESC`, so announced-but-unreleased albums sort to the *top* and ate the 50-album budget. the scanner skipped them *after* the budget was already spent → only a handful of released albums left → 5-10 tracks. fixed by fetching a generous budget (300) **and** excluding next-year albums at the query, so released albums fill the budget. the precise same-year `is_future_release` skip stays as a second guard. downstream caps (6/artist, top 75, take 50) unchanged.
+
+**tests:** 25 pure-core cases (consensus/similarity/recency) + 2 Fresh Tape regression tests, all green. full discovery suite (255) green. nothing else touched.
+
+---
+
+## best-in-class roadmap for listening recs (next phases — your call)
+
+these are the levers to take it further. ordered by value-to-risk. none are required; tonight's work stands on its own.
+
+| phase | what | value | risk | notes |
+|---|---|---|---|---|
+| **3** | **playable track row** ✅ DONE | high | low-med | shipped: "🎧 Your Listening Mix" row — a track playlist (play/queue/download/sync) right under the artist row. stored as full render-ready dicts (not pool-hydrated, so it can't shrink on pool rotation like Fresh Tape does). |
+| **4** | **direct top-tracks fetch** ✅ DONE | high | med | shipped: scan fetches each recommended artist's top tracks (Spotify/Deezer), resolving the artist id by name-search when the similar-artist row lacks one — guarded by a strict name-match so it never pulls the wrong artist. bounded (top 20 recs), per-call guarded, fail-soft to the pool. iTunes has no top-tracks API → pool-only there. needs a live scan to populate. |
+| **5** | **genre-affinity boost** | med | low | we already compute your genre breakdown. boost recs whose genres match your top genres → tighter taste alignment. pure scoring add. |
+| **6** | **adventurousness dial** | med | low | the ranker already supports `min_seed_count` (consensus floor). expose it as a "Safe ↔ Adventurous" slider on the row. |
+| **7** | **diversity pass** | low-med | low | avoid 40 recs all orbiting your single heaviest seed — cap picks-per-seed so the row spans your taste. |
+
+the core is built to absorb all of these without re-plumbing — `similarity_from_rank`, `build_recency_weighted_seeds`, and the scoring formula are all pure + tested.
+
+---
+
+## full discover-page generator audit (every soulsync-built row, excluding last.fm + listenbrainz)
+
+how each one is generated today, and whether it can be elevated. "clear win" = safe + additive. "product call" = needs your decision (changes the row's character).
+
+### curated (built during the scan, then hydrated)
+- **Fresh Tape / Release Radar** — new releases from watchlist+similar artists. **FIXED tonight** (see above). one more *clear win* available: hydration silently drops any curated id no longer in the discovery pool — could fall back to the stored `track_data_json` blob so the row can't shrink at read time.
+- **The Archives / Discovery Weekly** — strong already. nice 3-tier popularity split + serendipity scoring (boost never-played artists, penalize overplayed). same hydration-drop caveat as Fresh Tape; same cheap fallback fix.
+- **Seasonal Mix** — cleanest of the bunch. hydrates from a dedicated `seasonal_tracks` table (carries its own data), so it doesn't suffer the pool-drop problem. no bug.
+
+### discovery-pool generators (live queries)
+- **Popular Picks** — ranks by popularity DESC. solid. only nit: on iTunes (no popularity scale) it silently degrades to random — indistinguishable from Shuffle there. UI-label thing at most.
+- **Hidden Gems** — *clear win*. currently `ORDER BY RANDOM()` over low-popularity tracks — so it's "random obscure," not "*best* obscure." a light ranking (popularity just under the threshold, or genre-affinity to you) would make it feel curated instead of arbitrary. (a deeper *product call*: add personalization like Archives has — bigger lift, changes its "pure underground" character.)
+- **Genre Playlists** — good. pushes the genre match into SQL. `RANDOM()` ordering is fine for a browse; a popularity/affinity tiebreak (*clear win*) would make thin genres feel less arbitrary.
+- **Discovery Shuffle** — random by design, correct. only possible add: exclude tracks already shown in other rows this refresh (needs a cross-section seen-set — medium plumbing).
+- **Time Machine (by decade)** — *clear win, low risk*: decades are hardcoded, so a modern-only library shows 7 decade tabs, 5 empty. filter the tabs to decades that actually have pool data.
+- **Daily Mix** — the weakest row. the "50% your library" half permanently returns nothing (library tracks have no source ids to play), so each Daily Mix is really just a relabeled Genre Playlist. real fix = backfill source ids into library rows (*schema-level, higher risk*) — worth a dedicated pass, not a quick tweak. also silently falls back to "top artists as pseudo-genres" when genre data is missing → "Daily Mix 1" becomes mislabeled artist-radio. gate/label that (*clear win*).
+
+### cross-cutting
+- **hydration fragility** (Fresh Tape + Archives): both depend on curated ids still living in the pool at read time; misses are dropped silently. Seasonal already solved this with a dedicated table. giving the two spotify-style rows the same data-blob fallback is the single most robust cross-cutting fix. low risk, clear win.
+- **RANDOM-ordering pattern** (Hidden Gems, Shuffle, Genre, Decade): intentional for variety, but leaves quality signal on the table for the non-shuffle rows. adding a light ranking pass to Hidden Gems + Genre is the biggest "best-in-class" lever after tonight's work.
+
+want me to take any of these? the Hidden Gems ranking + Time Machine empty-decade filter + the Fresh Tape/Archives hydration fallback are all safe, additive, same-shape-as-tonight wins i can knock out next.

core/discovery/listening_recommendations.py

@@ -46,6 +46,67 @@ def _get(row: object, attr: str):
     return getattr(row, attr, None)
 
 
+def names_match(a: object, b: object) -> bool:
+    """Strict artist-name equality after stripping case + non-alphanumerics.
+
+    Used to verify a name-search result before fetching that artist's top tracks, so the
+    "Listening Mix" can never pull the WRONG artist's songs (e.g. a same-name act). Exact
+    alphanumeric match: "Tyler, The Creator" == "Tyler The Creator", but "Drake" != "Drake Bell".
+    Pure.
+    """
+    def _alnum(x: object) -> str:
+        return "".join(ch for ch in str(x or "").lower() if ch.isalnum())
+    na, nb = _alnum(a), _alnum(b)
+    return bool(na) and na == nb
+
+
+def similarity_from_rank(rank: object, max_rank: int = 10) -> float:
+    """Turn a stored ``similarity_rank`` (1 = most similar … 10 = least) into a 0–1 weight.
+
+    SoulSync stores each ``(seed → similar)`` edge with a 1–10 rank (``1`` is the closest
+    match). The ranker multiplies this into the score so a seed's *closest* matches count
+    for more than its long-tail ones. Linear decay over the documented range: rank 1 → 1.0,
+    rank 5 → 0.6, rank 10 → 0.1, with a 0.1 floor so a far match still contributes. A
+    missing/garbage rank falls back to 1.0 (treat as "no rank info, full weight"). Pure.
+    """
+    try:
+        r = int(rank)
+    except (TypeError, ValueError):
+        return 1.0
+    floor = round(1.0 / max_rank, 4)
+    if r <= 1:
+        return 1.0
+    if r >= max_rank:
+        return floor
+    return round((max_rank - r + 1) / max_rank, 4)
+
+
+def build_recency_weighted_seeds(
+    top_artists: Sequence[dict],
+    recent_play_counts: Optional[Dict[str, float]] = None,
+    *,
+    recency_factor: float = 1.5,
+) -> List[dict]:
+    """Blend lifetime + recent play counts into seed weights — "what you're into NOW".
+
+    ``weight = lifetime_plays + recency_factor × recent_plays``. An artist you've played a
+    lot *recently* outranks one you played a lot years ago, so the recommendations track
+    your current taste instead of your all-time history. ``recency_factor`` is the dial
+    (0 = pure lifetime). Returns ``[{'name', 'weight'}]`` for :func:`rank_recommended_artists`.
+    Pure — the caller supplies both play-count maps from the listening history.
+    """
+    recent = {_norm(k): _positive_float(v, 0.0) for k, v in (recent_play_counts or {}).items()}
+    out: List[dict] = []
+    for a in top_artists or ():
+        name = str(a.get("name") or "").strip()
+        if not name:
+            continue
+        lifetime = _positive_float(a.get("play_count", a.get("weight", 1.0)))
+        boost = recency_factor * recent.get(_norm(name), 0.0)
+        out.append({"name": name, "weight": lifetime + boost})
+    return out
+
+
 def group_similars_by_seed(
     seeds: Sequence[dict],
     similar_rows: Sequence,
@@ -53,14 +114,21 @@ def group_similars_by_seed(
     *,
     source_id_attr: str = "source_artist_id",
     similar_name_attr: str = "similar_artist_name",
+    rank_attr: Optional[str] = None,
 ) -> Dict[str, List[dict]]:
-    """Reshape flat ``similar_artists`` rows into ``{seed_name_lower: [{'name': similar}]}``.
+    """Reshape flat ``similar_artists`` rows into ``{seed_name_lower: [{'name', 'score'?}]}``.
 
     The stored rows key the similar artist by the SEED's source id (``source_artist_id``),
     not its name, so :func:`rank_recommended_artists` can't consume them directly. This
     resolves each row's source id to a name via ``id_to_name`` (``{source_artist_id:
     artist_name}`` for the library, built by the caller) and keeps only rows that resolve
     to one of the ``seeds``. Rows may be dataclass objects or dicts. Pure — no I/O.
+
+    ``id_to_name`` MUST be keyed by whatever id the edges actually store — for SoulSync that
+    is the artist's SOURCE id (Spotify/iTunes/Deezer/MusicBrainz), NOT the internal row id.
+    When ``rank_attr`` is given, each row's rank is converted via :func:`similarity_from_rank`
+    and carried as ``score`` so closer matches weigh more; without it every similar comes out
+    score-less (the ranker then treats similarity as 1.0 — original behavior).
     """
     seed_names = {_norm(s.get("name")) for s in seeds}
     seed_names.discard("")
@@ -72,8 +140,12 @@ def group_similars_by_seed(
         if not seed_name or seed_name not in seed_names:
             continue
         sim_name = str(_get(row, similar_name_attr) or "").strip()
-        if sim_name:
+        if not sim_name:
-            out.setdefault(seed_name, []).append({"name": sim_name})
+            continue
+        entry = {"name": sim_name}
+        if rank_attr is not None:
+            entry["score"] = similarity_from_rank(_get(row, rank_attr))
+        out.setdefault(seed_name, []).append(entry)
     return out
 
 
@@ -197,8 +269,56 @@ def aggregate_candidate_tracks(
     return out[:limit]
 
 
+def to_mix_track(track: object, source: str) -> Optional[dict]:
+    """Shape one source "top tracks" API dict into the flat dict the Discover compact
+    playlist row renders + syncs (the "Listening Mix" #913 playlist).
+
+    Spotify's ``artist_top_tracks`` and Deezer's ``get_artist_top_tracks`` both return the
+    same Spotify-shape object (``id, name, artists[], album{name,images[]}, duration_ms``).
+    This flattens that into the renderer's field names (``track_name/artist_name/album_name/
+    album_cover_url/duration_ms``), keeps the original under ``track_data_json`` for sync, and
+    sets the source-specific id field. Returns None for anything without a usable id/title so
+    the caller can filter. A ``name`` key is kept so :func:`aggregate_candidate_tracks` can
+    dedup by title. Pure — no I/O.
+    """
+    if not isinstance(track, dict):
+        return None
+    tid = track.get("id")
+    name = str(track.get("name") or "").strip()
+    if not tid or not name:
+        return None
+    artists = track.get("artists") or []
+    artist_name = ""
+    if artists and isinstance(artists[0], dict):
+        artist_name = str(artists[0].get("name") or "").strip()
+    album = track.get("album") if isinstance(track.get("album"), dict) else {}
+    album_name = str(album.get("name") or "").strip()
+    images = album.get("images") or []
+    cover = images[0].get("url") if images and isinstance(images[0], dict) else None
+    out = {
+        "track_id": str(tid),
+        "name": name,                  # for aggregate_candidate_tracks dedup
+        "track_name": name,            # for the renderer
+        "artist_name": artist_name,
+        "album_name": album_name,
+        "album_cover_url": cover,
+        "duration_ms": track.get("duration_ms") or 0,
+        "track_data_json": track,      # full payload for sync/download
+        "source": source,
+    }
+    id_field = {"spotify": "spotify_track_id", "deezer": "deezer_track_id",
+                "itunes": "itunes_track_id"}.get(source)
+    if id_field:
+        out[id_field] = str(tid)
+    return out
+
+
 __all__ = [
     "RecommendedArtist",
+    "names_match",
+    "similarity_from_rank",
+    "build_recency_weighted_seeds",
+    "to_mix_track",
     "group_similars_by_seed",
     "rank_recommended_artists",
     "aggregate_candidate_tracks",

core/watchlist_scanner.py

@@ -3633,8 +3633,13 @@ class WatchlistScanner:
             for source in sources_to_process:
                 logger.info(f"Curating Release Radar for {source}...")
 
-                # 1. Curate Release Radar - 50 tracks from recent albums
+                # 1. Curate Release Radar - 50 tracks from recent albums.
-                recent_albums = self.database.get_discovery_recent_albums(limit=50, source=source, profile_id=profile_id)
+                # Fetch a GENEROUS album budget (not 50) and exclude next-year announcements at the
+                # query: future-dated albums sort to the top of release_date DESC and used to eat the
+                # budget before the in-loop is_future_release skip ran, starving Fresh Tape to a few
+                # tracks. The downstream caps (6/artist, top 75, take 50) still bound the output.
+                recent_albums = self.database.get_discovery_recent_albums(
+                    limit=300, source=source, profile_id=profile_id, exclude_future_years=True)
                 release_radar_tracks = []
 
                 if not recent_albums:
@@ -4004,45 +4009,129 @@ class WatchlistScanner:
         The ranking lives in core.discovery.listening_recommendations (pure + tested); this only
         gathers inputs — all already in the DB, NO new network — and stores the result under NEW
         metadata/curated keys. Fully self-contained and guarded: any failure logs and returns, so
-        it can never disturb the scan. Phase-1 candidate tracks come from the discovery pool (like
+        it can never disturb the scan.
-        BYLT); a later phase swaps in a direct top-tracks fetch for richer coverage.
+
+        "Best in class" generation (the elevation over the first cut):
+          • Seeds are recency-weighted — recent plays boost lifetime favourites so the picks
+            track what you're into NOW, not just all-time totals.
+          • The ranker is fed the RAW per-seed edges (one row per seed→similar), so an artist
+            similar to several of your seeds accumulates real CONSENSUS — the old code fed the
+            name-collapsed ``get_top_similar_artists`` query, which flattened every similar to a
+            single seed (consensus could never fire). The raw edges also carry ``similarity_rank``,
+            so a seed's CLOSEST matches outweigh its long-tail ones.
+          • ``source_artist_id`` is a SOURCE id (Spotify/iTunes/Deezer/MusicBrainz), so the id→name
+            map is built from the artists' source-id columns, NOT the internal row id (the first
+            cut keyed it by ``artists.id`` and resolved nothing — the feature produced 0 recs).
+        Phase-1 candidate tracks still come from the discovery pool (like BYLT); a later phase
+        swaps in a direct top-tracks fetch for richer coverage.
         """
         try:
             import json as _json
             from core.discovery.listening_recommendations import (
                 aggregate_candidate_tracks,
+                build_recency_weighted_seeds,
                 group_similars_by_seed,
+                names_match,
                 rank_recommended_artists,
+                to_mix_track,
             )
 
-            seeds = [{'name': s['name'], 'weight': s.get('play_count', 1)}
+            # Recency-weighted seeds: lifetime top artists, boosted by recent (30d) plays.
-                     for s in (self.database.get_top_artists('all', 30) or []) if s.get('name')]
+            lifetime = [s for s in (self.database.get_top_artists('all', 30) or []) if s.get('name')]
-            if not seeds:
+            if not lifetime:
                 return
+            recent_rows = self.database.get_top_artists('30d', 50) or []
+            recent_counts = {r['name'].lower(): r.get('play_count', 0)
+                             for r in recent_rows if r.get('name')}
+            seeds = build_recency_weighted_seeds(lifetime, recent_counts)
+            seed_names = {s['name'].lower() for s in seeds}
 
-            # id -> name + owned-artist set for the WHOLE library (similar_artists rows key the
+            # Owned-artist set (for exclusion) + the seeds' SOURCE ids (similar_artists.source_artist_id
-            # similar artist by the seed artist's id).
+            # is a Spotify/iTunes/Deezer/MusicBrainz id, never the internal artists.id). We only need
-            id_to_name, owned = {}, set()
+            # id→name for the SEED ids, since the edge query below is already scoped to them.
+            owned, seed_source_ids, seed_id_to_name = set(), [], {}
             with self.database._get_connection() as conn:
                 cur = conn.cursor()
-                cur.execute("SELECT id, name FROM artists WHERE name IS NOT NULL AND name != ''")
+                cur.execute("SELECT name, spotify_artist_id, itunes_artist_id, deezer_id, "
+                            "musicbrainz_id FROM artists WHERE name IS NOT NULL AND name != ''")
                 for row in cur.fetchall():
-                    id_to_name[str(row[0])] = row[1]
+                    nm = row[0]
-                    owned.add((row[1] or '').lower())
+                    lname = (nm or '').lower()
+                    owned.add(lname)
+                    if lname in seed_names:
+                        for sid in (row[1], row[2], row[3], row[4]):
+                            if sid:
+                                seed_source_ids.append(str(sid))
+                                seed_id_to_name[str(sid)] = nm
+
+                # RAW per-seed edges (preserve consensus + similarity_rank). Scoped to the seeds.
+                edges, edge_cols = [], ('source_artist_id', 'similar_artist_name', 'similarity_rank',
+                                       'spotify_id', 'itunes_id', 'deezer_id', 'image_url', 'genres')
+                if seed_source_ids:
+                    placeholders = ",".join("?" * len(seed_source_ids))
+                    cur.execute(
+                        f"SELECT source_artist_id, similar_artist_name, similarity_rank, "
+                        f"similar_artist_spotify_id, similar_artist_itunes_id, "
+                        f"similar_artist_deezer_id, image_url, genres FROM similar_artists "
+                        f"WHERE profile_id = ? AND source_artist_id IN ({placeholders})",
+                        [profile_id, *seed_source_ids])
+                    edges = [dict(zip(edge_cols, r, strict=False)) for r in cur.fetchall()]
+
+            # Per-name enrichment (image/ids/genres) so the Discover row can render rich cards.
+            artist_meta_by_name = {}
+            for e in edges:
+                key = (e['similar_artist_name'] or '').lower()
+                if not key:
+                    continue
+                m = artist_meta_by_name.setdefault(key, {})
+                for src_k, dst_k in (('spotify_id', 'spotify_artist_id'),
+                                     ('itunes_id', 'itunes_artist_id'),
+                                     ('deezer_id', 'deezer_artist_id')):
+                    if e.get(src_k) and not m.get(dst_k):
+                        m[dst_k] = e[src_k]
+                if e.get('image_url') and not m.get('image_url'):
+                    m['image_url'] = e['image_url']
+                if e.get('genres') and not m.get('genres'):
+                    m['genres'] = e['genres']
+
+            similars_by_seed = group_similars_by_seed(
+                seeds, edges, seed_id_to_name, rank_attr='similarity_rank')
+
+            # Fallback: if the raw edges resolved nothing (e.g. source ids not yet populated),
+            # degrade to the aggregated query so the feature still works rather than going dark.
+            if not similars_by_seed:
+                agg = self.database.get_top_similar_artists(limit=1000, profile_id=profile_id)
+                similars_by_seed = group_similars_by_seed(
+                    seeds, agg, seed_id_to_name, rank_attr='similarity_rank')
 
-            similar_rows = self.database.get_top_similar_artists(limit=1000, profile_id=profile_id)
-            similars_by_seed = group_similars_by_seed(seeds, similar_rows, id_to_name)
             recs = rank_recommended_artists(seeds, similars_by_seed, owned, limit=40)
             if not recs:
                 logger.info("[Listening Recs] no recommendations yet (no similar-artist coverage)")
                 return
 
-            self.database.set_metadata('listening_recs_artists', _json.dumps([
+            def _enrich(r):
-                {'name': r.name, 'seed_count': r.seed_count, 'seeds': r.seeds[:5], 'score': r.score}
+                m = artist_meta_by_name.get(r.name.lower(), {})
-                for r in recs
+                genres = m.get('genres')
-            ]))
+                if isinstance(genres, str):
+                    try:
+                        genres = _json.loads(genres)
+                    except Exception:
+                        genres = None
+                return {'name': r.name, 'seed_count': r.seed_count, 'seeds': r.seeds[:5],
+                        'score': r.score, 'spotify_artist_id': m.get('spotify_artist_id'),
+                        'itunes_artist_id': m.get('itunes_artist_id'),
+                        'deezer_artist_id': m.get('deezer_artist_id'),
+                        'image_url': m.get('image_url'),
+                        'genres': (genres[:3] if isinstance(genres, list) else None)}
 
-            # Candidate tracks from the discovery pool grouped by artist (phase 1: no new network).
+            self.database.set_metadata('listening_recs_artists',
+                                       _json.dumps([_enrich(r) for r in recs]))
+
+            # Candidate tracks for the "Listening Mix" playlist row: each recommended artist's
+            # top tracks. Prefer a DIRECT top-tracks fetch (Spotify/Deezer — richest + most
+            # accurate), fall back to the discovery pool (covers iTunes + any artist the fetch
+            # missed). Stored as full render-ready dicts so the row needs NO pool re-hydration —
+            # robust against pool rotation (the bug that shrinks Fresh Tape/Archives at read time).
             pool, active_source = [], None
             for src in (sources_to_process or []):
                 pool = self.database.get_discovery_pool_tracks(
@@ -4050,24 +4139,85 @@ class WatchlistScanner:
                 if pool:
                     active_source = src
                     break
+            if not active_source:
+                active_source = (sources_to_process or ['spotify'])[0]
 
-            track_ids = []
+            # Pool baseline grouped by artist (full render dicts), no network.
-            if pool:
+            pool_by_artist = {}
-                by_artist = {}
+            for t in (pool or []):
-                for t in pool:
+                an = (getattr(t, 'artist_name', '') or '').lower()
-                    an = (getattr(t, 'artist_name', '') or '').lower()
+                tid = (getattr(t, 'spotify_track_id', None) if active_source == 'spotify'
-                    tid = (getattr(t, 'spotify_track_id', None) if active_source == 'spotify'
+                       else getattr(t, 'itunes_track_id', None) if active_source == 'itunes'
-                           else getattr(t, 'itunes_track_id', None) if active_source == 'itunes'
+                       else getattr(t, 'deezer_track_id', None))
-                           else getattr(t, 'deezer_track_id', None))
+                name = getattr(t, 'track_name', '') or ''
-                    if an and tid:
+                if not an or not tid or not name:
-                        by_artist.setdefault(an, []).append({'name': getattr(t, 'track_name', ''), 'id': tid})
+                    continue
-                candidates = aggregate_candidate_tracks(recs, by_artist, per_artist=3, limit=50)
+                tdj = getattr(t, 'track_data_json', None)
-                track_ids = [c['id'] for c in candidates if c.get('id')]
+                if isinstance(tdj, str):
-                if track_ids:
+                    try:
-                    self.database.save_curated_playlist('listening_recs_tracks', track_ids, profile_id=profile_id)
+                        tdj = _json.loads(tdj)
+                    except Exception:
+                        tdj = None
+                pool_by_artist.setdefault(an, []).append({
+                    'track_id': str(tid), 'name': name, 'track_name': name,
+                    'artist_name': getattr(t, 'artist_name', '') or '',
+                    'album_name': getattr(t, 'album_name', '') or '',
+                    'album_cover_url': getattr(t, 'album_cover_url', None),
+                    'duration_ms': getattr(t, 'duration_ms', 0) or 0,
+                    'track_data_json': tdj, 'source': active_source,
+                    f'{active_source}_track_id': str(tid)})
 
-            logger.info("[Listening Recs] %d recommended artists, %d candidate tracks",
+            # Direct top-tracks enrichment — guarded, bounded (top 20 recs), fail-soft per artist.
-                        len(recs), len(track_ids))
+            fetched_by_artist = {}
+            try:
+                if active_source in ('spotify', 'deezer'):
+                    client = get_client_for_source(active_source)
+                    if client and hasattr(client, 'get_artist_top_tracks'):
+                        id_key = 'spotify_artist_id' if active_source == 'spotify' else 'deezer_artist_id'
+                        can_search = hasattr(client, 'search_artists')
+                        for r in recs[:20]:
+                            aid = artist_meta_by_name.get(r.name.lower(), {}).get(id_key)
+                            # Most similar-artist rows store a name but no source id, so resolve
+                            # it by name-search — guarded by names_match so we never fetch the
+                            # WRONG artist's tracks (a same-name act).
+                            if not aid and can_search:
+                                try:
+                                    found = client.search_artists(r.name, limit=1) or []
+                                except Exception as _s_err:
+                                    logger.debug("[Listening Recs] artist search failed for %s: %s",
+                                                 r.name, _s_err)
+                                    found = []
+                                if found and names_match(r.name, getattr(found[0], 'name', '')):
+                                    aid = getattr(found[0], 'id', None)
+                            if not aid:
+                                continue
+                            try:
+                                raw = client.get_artist_top_tracks(str(aid), limit=8) or []
+                            except Exception as _tt_err:
+                                logger.debug("[Listening Recs] top-tracks fetch failed for %s: %s",
+                                             r.name, _tt_err)
+                                continue
+                            shaped = [s for s in (to_mix_track(x, active_source) for x in raw) if s][:5]
+                            if shaped:
+                                fetched_by_artist[r.name.lower()] = shaped
+            except Exception as _enr_err:
+                logger.debug("[Listening Recs] top-tracks enrichment skipped: %s", _enr_err)
+
+            # Merge: prefer fetched top tracks, fall back to the pool per artist.
+            top_tracks_by_artist = {}
+            for r in recs:
+                merged = fetched_by_artist.get(r.name.lower()) or pool_by_artist.get(r.name.lower())
+                if merged:
+                    top_tracks_by_artist[r.name.lower()] = merged
+
+            mix = aggregate_candidate_tracks(recs, top_tracks_by_artist, per_artist=3, limit=50)
+            track_ids = [m.get('track_id') for m in mix if m.get('track_id')]
+            if mix:
+                self.database.set_metadata('listening_recs_tracks_full', _json.dumps(mix))
+                self.database.save_curated_playlist('listening_recs_tracks', track_ids, profile_id=profile_id)
+
+            logger.info("[Listening Recs] %d recommended artists, %d mix tracks (%d artists via top-tracks fetch)",
+                        len(recs), len(mix), len(fetched_by_artist))
         except Exception as e:
             logger.debug("[Listening Recs] generation skipped: %s", e)
 

database/music_database.py

@@ -10884,23 +10884,40 @@ class MusicDatabase:
             logger.error(f"Error caching discovery recent album: {e}")
             return False
 
-    def get_discovery_recent_albums(self, limit: int = 10, source: Optional[str] = None, profile_id: int = 1) -> List[Dict[str, Any]]:
+    def get_discovery_recent_albums(self, limit: int = 10, source: Optional[str] = None, profile_id: int = 1,
-        """Get cached recent albums for discover page, optionally filtered by source"""
+                                    exclude_future_years: bool = False) -> List[Dict[str, Any]]:
+        """Get cached recent albums for discover page, optionally filtered by source.
+
+        exclude_future_years: drop announced-but-unreleased albums dated to a LATER YEAR.
+        Because rows are ordered ``release_date DESC``, future-dated albums otherwise sort to
+        the very top and consume the ``limit`` budget — which is exactly why Fresh Tape / Release
+        Radar starved down to a handful of tracks. Year-level so it's precision-safe across
+        'YYYY' / 'YYYY-MM' / 'YYYY-MM-DD'; same-year future months are left for the caller's precise
+        ``is_future_release`` check. NULL/blank dates are kept (treated as released).
+        """
         try:
             with self._get_connection() as conn:
                 cursor = conn.cursor()
 
+                future_clause = ""
+                if exclude_future_years:
+                    future_clause = (
+                        " AND (release_date IS NULL OR release_date = '' "
+                        "OR CAST(substr(release_date, 1, 4) AS INTEGER) "
+                        "<= CAST(strftime('%Y','now') AS INTEGER))"
+                    )
+
                 if source:
-                    cursor.execute("""
+                    cursor.execute(f"""
                         SELECT * FROM discovery_recent_albums
-                        WHERE source = ? AND profile_id = ?
+                        WHERE source = ? AND profile_id = ?{future_clause}
                         ORDER BY release_date DESC
                         LIMIT ?
                     """, (source, profile_id, limit))
                 else:
-                    cursor.execute("""
+                    cursor.execute(f"""
                         SELECT * FROM discovery_recent_albums
-                        WHERE profile_id = ?
+                        WHERE profile_id = ?{future_clause}
                         ORDER BY release_date DESC
                         LIMIT ?
                     """, (profile_id, limit))

tests/discovery/test_listening_recommendations.py

@@ -4,14 +4,67 @@ from __future__ import annotations
 
 from core.discovery.listening_recommendations import (
     aggregate_candidate_tracks,
+    build_recency_weighted_seeds,
+    names_match,
     rank_recommended_artists,
+    similarity_from_rank,
+    to_mix_track,
 )
 
 
+# ── names_match (guards the top-tracks fetch against wrong-artist results) ─────
+def test_names_match_ignores_case_and_punctuation():
+    assert names_match("Tyler, The Creator", "Tyler The Creator")
+    assert names_match("BEYONCÉ", "beyoncé")
+    assert names_match("AC/DC", "ac dc")
+
+
+def test_names_match_rejects_near_misses_and_empty():
+    assert not names_match("Drake", "Drake Bell")
+    assert not names_match("", "anything")
+    assert not names_match("X", None)
+
+
 def _seed(name, weight=1.0):
     return {"name": name, "weight": weight}
 
 
+# ── similarity_from_rank (1=closest .. 10=farthest -> 1.0 .. 0.1) ─────────────
+def test_similarity_from_rank_decays_over_documented_range():
+    assert similarity_from_rank(1) == 1.0
+    assert similarity_from_rank(5) == 0.6
+    assert similarity_from_rank(10) == 0.1
+
+
+def test_similarity_from_rank_clamps_and_defaults():
+    assert similarity_from_rank(0) == 1.0          # <=1 -> full weight
+    assert similarity_from_rank(50) == 0.1         # beyond range -> floor
+    assert similarity_from_rank(None) == 1.0       # missing -> full weight (no rank info)
+    assert similarity_from_rank("nan") == 1.0
+
+
+# ── build_recency_weighted_seeds (lifetime + factor*recent) ───────────────────
+def test_recency_boost_reorders_toward_current_taste():
+    # Old-fav has more lifetime plays, but New-fav dominates recently.
+    lifetime = [{"name": "OldFav", "play_count": 100}, {"name": "NewFav", "play_count": 40}]
+    recent = {"newfav": 60}
+    seeds = build_recency_weighted_seeds(lifetime, recent, recency_factor=1.5)
+    by = {s["name"]: s["weight"] for s in seeds}
+    assert by["OldFav"] == 100.0                   # no recent plays -> unchanged
+    assert by["NewFav"] == 40 + 1.5 * 60           # 130 -> now outranks OldFav
+
+
+def test_recency_factor_zero_is_pure_lifetime():
+    seeds = build_recency_weighted_seeds(
+        [{"name": "A", "play_count": 7}], {"a": 99}, recency_factor=0)
+    assert seeds == [{"name": "A", "weight": 7.0}]
+
+
+def test_recency_seeds_skip_blank_names_and_tolerate_missing_recent():
+    seeds = build_recency_weighted_seeds([{"name": ""}, {"name": "A", "play_count": 3}])
+    assert seeds == [{"name": "A", "weight": 3.0}]
+
+
 # ── rank_recommended_artists ─────────────────────────────────────────────────
 def test_consensus_outranks_single_endorsement():
     # 'Common' is similar to BOTH seeds; 'Solo' to one. Equal weights/scores.
@@ -117,6 +170,46 @@ def test_aggregate_skips_artist_with_no_tracks():
     assert [t["name"] for t in out] == ["only"]   # sim-b had no tracks -> skipped
 
 
+# ── to_mix_track (source top-track dict -> Discover compact-row dict) ─────────
+def _sp_track(tid="t1", name="Song", artist="Artist", album="Album", cover="http://cdn/c.jpg"):
+    return {"id": tid, "name": name, "artists": [{"name": artist}],
+            "album": {"name": album, "images": ([{"url": cover}] if cover else [])},
+            "duration_ms": 210000, "popularity": 55}
+
+
+def test_to_mix_track_shapes_render_fields():
+    out = to_mix_track(_sp_track(), "spotify")
+    assert out["track_name"] == "Song" and out["artist_name"] == "Artist"
+    assert out["album_name"] == "Album" and out["album_cover_url"] == "http://cdn/c.jpg"
+    assert out["duration_ms"] == 210000
+    assert out["spotify_track_id"] == "t1" and out["track_id"] == "t1"
+    assert out["track_data_json"]["id"] == "t1"      # full payload kept for sync
+    assert out["name"] == "Song"                     # kept for aggregate dedup
+
+
+def test_to_mix_track_source_id_field_per_source():
+    assert to_mix_track(_sp_track(), "deezer")["deezer_track_id"] == "t1"
+    assert to_mix_track(_sp_track(), "itunes")["itunes_track_id"] == "t1"
+
+
+def test_to_mix_track_rejects_unusable_and_tolerates_missing_album():
+    assert to_mix_track({"id": "x"}, "spotify") is None        # no title
+    assert to_mix_track({"name": "y"}, "spotify") is None       # no id
+    assert to_mix_track("garbage", "spotify") is None
+    bare = to_mix_track({"id": "z", "name": "Z"}, "spotify")    # no artists/album
+    assert bare["artist_name"] == "" and bare["album_cover_url"] is None
+
+
+def test_to_mix_track_feeds_aggregate_end_to_end():
+    # The real pipeline: shape source tracks, then aggregate by recommended artist.
+    recs = _recs("A")  # recommends 'sim-A'
+    shaped = [to_mix_track(_sp_track(tid="1", name="One", artist="sim-A"), "spotify"),
+              to_mix_track(_sp_track(tid="2", name="Two", artist="sim-A"), "spotify")]
+    out = aggregate_candidate_tracks(recs, {"sim-a": shaped}, per_artist=5, limit=10)
+    assert [t["track_name"] for t in out] == ["One", "Two"]
+    assert out[0]["spotify_track_id"] == "1"
+
+
 # ── group_similars_by_seed (id->name join) ───────────────────────────────────
 from dataclasses import dataclass as _dc  # noqa: E402
 
@@ -167,3 +260,37 @@ def test_group_then_rank_end_to_end():
     ranked = rank_recommended_artists(seeds, grouped, owned_artist_names={"solo"})
     assert ranked[0].name == "Common" and ranked[0].seed_count == 2
     assert all(r.name != "Solo" for r in ranked)   # owned excluded
+
+
+# ── rank-aware grouping (similarity_rank -> score) ────────────────────────────
+@_dc
+class _RankRow:
+    source_artist_id: str
+    similar_artist_name: str
+    similarity_rank: int
+
+
+def test_group_with_rank_attr_carries_similarity_score():
+    seeds = [_seed("A")]
+    rows = [_RankRow("ia", "Close", 1), _RankRow("ia", "Far", 10)]
+    out = group_similars_by_seed(seeds, rows, {"ia": "A"}, rank_attr="similarity_rank")
+    by = {e["name"]: e["score"] for e in out["a"]}
+    assert by == {"Close": 1.0, "Far": 0.1}
+
+
+def test_group_without_rank_attr_is_scoreless_backcompat():
+    seeds = [_seed("A")]
+    rows = [_RankRow("ia", "X", 3)]
+    out = group_similars_by_seed(seeds, rows, {"ia": "A"})
+    assert out["a"] == [{"name": "X"}]            # no score key -> original behavior
+
+
+def test_rank_threading_changes_winner_within_a_seed():
+    # The production fix: a CLOSER match (rank 1) on a heavy seed beats a far match (rank 9),
+    # even though both come from the same seed. Without rank threading they'd tie.
+    seeds = [_seed("Fav", weight=10)]
+    rows = [_RankRow("if", "Close", 1), _RankRow("if", "Far", 9)]
+    grouped = group_similars_by_seed(seeds, rows, {"if": "Fav"}, rank_attr="similarity_rank")
+    ranked = rank_recommended_artists(seeds, grouped)
+    assert [r.name for r in ranked] == ["Close", "Far"]
+    assert ranked[0].score > ranked[1].score

tests/discovery/test_recent_albums_future_filter.py

@@ -0,0 +1,57 @@
+"""Fresh Tape / Release Radar candidate fetch must not be starved by future albums.
+
+Regression: get_discovery_recent_albums orders release_date DESC, so announced-but-
+unreleased albums dated to a LATER YEAR sort to the very top and consumed the album
+budget before the scanner's in-loop is_future_release skip ran — leaving only a handful
+of released albums to draw tracks from (the reported "Fresh Tape only has 5-10 tracks").
+exclude_future_years drops next-year albums at the query so released ones fill the budget.
+"""
+
+from __future__ import annotations
+
+from datetime import datetime
+
+from database.music_database import MusicDatabase
+
+
+def _insert(db, **kw):
+    with db._get_connection() as conn:
+        conn.execute(
+            """INSERT INTO discovery_recent_albums
+               (album_spotify_id, album_name, artist_name, artist_spotify_id,
+                album_cover_url, release_date, album_type, source, profile_id)
+               VALUES (?,?,?,?,?,?,?,?,?)""",
+            (kw['id'], kw['name'], kw['artist'], kw['id'] + '_a', '',
+             kw['release_date'], kw.get('album_type', 'album'), kw.get('source', 'spotify'), 1))
+        conn.commit()
+
+
+def test_future_year_albums_excluded_released_kept(tmp_path):
+    db = MusicDatabase(database_path=str(tmp_path / "t.db"))
+    this_year = datetime.now().year
+    next_year = this_year + 1
+    _insert(db, id='past1', name='Released A', artist='X', release_date=f'{this_year - 1}-03-01')
+    _insert(db, id='past2', name='Released B', artist='Y', release_date=f'{this_year}-01-15')
+    _insert(db, id='fut1', name='Announced', artist='Z', release_date=f'{next_year}-02-01')
+    _insert(db, id='fut2', name='Year Only Future', artist='W', release_date=str(next_year))
+    _insert(db, id='blank', name='Unknown Date', artist='Q', release_date='')
+
+    names_all = {a['album_name'] for a in db.get_discovery_recent_albums(limit=50, source='spotify')}
+    assert 'Announced' in names_all          # without the flag, futures are present (and sort first)
+
+    filtered = db.get_discovery_recent_albums(limit=50, source='spotify', exclude_future_years=True)
+    names = {a['album_name'] for a in filtered}
+    assert 'Announced' not in names          # next-year album dropped
+    assert 'Year Only Future' not in names   # YYYY-only future dropped
+    assert {'Released A', 'Released B'} <= names   # released kept
+    assert 'Unknown Date' in names           # blank date kept (treated as released)
+
+
+def test_future_filter_does_not_over_trim_when_all_released(tmp_path):
+    db = MusicDatabase(database_path=str(tmp_path / "t2.db"))
+    this_year = datetime.now().year
+    for i in range(8):
+        _insert(db, id=f'r{i}', name=f'Album {i}', artist=f'A{i}',
+                release_date=f'{this_year}-0{(i % 9) + 1}-01')
+    filtered = db.get_discovery_recent_albums(limit=300, source='spotify', exclude_future_years=True)
+    assert len(filtered) == 8                # every released album survives, budget honored

tests/test_watchlist_scanner_scan.py

@@ -1171,7 +1171,7 @@ def test_curate_discovery_playlists_uses_source_priority_for_recent_albums(monke
         "avg_daily_plays": 0.0,
         "artist_play_counts": {},
     })
-    monkeypatch.setattr(scanner.database, "get_discovery_recent_albums", lambda limit, source, profile_id: [recent_album] if source == "deezer" else [], raising=False)
+    monkeypatch.setattr(scanner.database, "get_discovery_recent_albums", lambda limit, source, profile_id, exclude_future_years=False: [recent_album] if source == "deezer" else [], raising=False)
     monkeypatch.setattr(scanner.database, "get_discovery_pool_tracks", lambda *args, **kwargs: [discovery_track] if kwargs.get("source") == "deezer" else [], raising=False)
     monkeypatch.setattr(scanner.database, "save_curated_playlist", lambda key, tracks, profile_id=1: saved_playlists.append((key, list(tracks))) or True, raising=False)
     monkeypatch.setattr(scanner.database, "get_top_artists", lambda *args, **kwargs: [], raising=False)

web_server.py

@@ -29083,6 +29083,96 @@ def get_discover_similar_artists():
         return jsonify({"success": False, "error": str(e)}), 500
 
 
+@app.route('/api/discover/listening-recommendations', methods=['GET'])
+def get_discover_listening_recommendations():
+    """#913: artists you'd love based on what you actually LISTEN to (play-weighted).
+
+    Distinct from /api/discover/similar-artists (which is driven by your whole library /
+    watchlist): this is seeded by your most-PLAYED artists, consensus-ranked across the
+    similar-artist graph, and recency-boosted. The heavy lifting + storage happen during the
+    watchlist scan (core.watchlist_scanner._build_listening_recommendations -> the
+    'listening_recs_artists' metadata key); this endpoint just reshapes the stored list to the
+    same card shape the recommended-artists row already renders. Read-only, fail-soft.
+    """
+    try:
+        database = get_database()
+        active_source = _get_active_discovery_source()
+        raw = database.get_metadata('listening_recs_artists')
+        if not raw:
+            return jsonify({"success": True, "artists": [], "source": active_source, "count": 0})
+        try:
+            stored = json.loads(raw) or []
+        except (ValueError, TypeError):
+            stored = []
+
+        result_artists = []
+        for a in stored:
+            name = a.get('name')
+            if not name:
+                continue
+            if active_source == 'spotify':
+                artist_id = a.get('spotify_artist_id')
+            elif active_source == 'deezer':
+                artist_id = a.get('deezer_artist_id') or a.get('itunes_artist_id')
+            else:
+                artist_id = a.get('itunes_artist_id')
+            entry = {
+                "artist_id": artist_id,
+                "spotify_artist_id": a.get('spotify_artist_id'),
+                "itunes_artist_id": a.get('itunes_artist_id'),
+                "deezer_artist_id": a.get('deezer_artist_id'),
+                "artist_name": name,
+                "seed_count": a.get('seed_count'),
+                "source": active_source,
+            }
+            img = a.get('image_url')
+            if img:
+                entry["image_url"] = fix_artist_image_url(img)
+            if a.get('genres'):
+                entry["genres"] = a['genres'][:3]
+            # "because you listen to X, Y, Z" — the most-played artists that point here.
+            if a.get('seeds'):
+                entry["because"] = a['seeds']
+            result_artists.append(entry)
+
+        return jsonify({
+            "success": True,
+            "artists": result_artists,
+            "source": active_source,
+            "count": len(result_artists),
+        })
+    except Exception as e:
+        logger.error(f"Error getting listening recommendations: {e}")
+        return jsonify({"success": False, "error": str(e)}), 500
+
+
+@app.route('/api/discover/personalized/listening-mix', methods=['GET'])
+def get_discover_listening_mix():
+    """#913: the "Listening Mix" playlist row — a playable track mix from the artists you'd
+    love based on what you actually listen to.
+
+    The tracks are built during the watchlist scan (core.watchlist_scanner
+    ._build_listening_recommendations -> the 'listening_recs_tracks_full' metadata key) as full
+    render-ready dicts, so this endpoint just hands them back — no discovery-pool re-hydration,
+    which means it can't shrink when the pool rotates (the failure mode Fresh Tape/Archives hit).
+    Same {success, tracks} shape renderCompactPlaylist + the sync/download chains expect.
+    """
+    try:
+        database = get_database()
+        active_source = _get_active_discovery_source()
+        raw = database.get_metadata('listening_recs_tracks_full')
+        tracks = []
+        if raw:
+            try:
+                tracks = json.loads(raw) or []
+            except (ValueError, TypeError):
+                tracks = []
+        return jsonify({"success": True, "tracks": tracks, "source": active_source})
+    except Exception as e:
+        logger.error(f"Error getting listening mix: {e}")
+        return jsonify({"success": False, "error": str(e)}), 500
+
+
 @app.route('/api/discover/similar-artists/enrich', methods=['POST'])
 def enrich_similar_artists():
     """Enrich a batch of artist IDs with images/genres from Spotify or iTunes.

webui/index.html

@@ -3133,6 +3133,53 @@
                     </div>
 
                     <!-- Recommended For You Section (similar-artists graph) -->
+                    <!-- #913: listening-driven recommendations (play-weighted, consensus-ranked) -->
+                    <div class="discover-section" id="listening-recs-section" style="display: none;">
+                        <div class="discover-section-header">
+                            <div>
+                                <h2 class="discover-section-title">Based On Your Listening</h2>
+                                <p class="discover-section-subtitle">Artists you'd love — ranked from who you actually play the most</p>
+                            </div>
+                        </div>
+                        <div class="discover-carousel" id="listening-recs-carousel">
+                            <!-- Populated by JS -->
+                        </div>
+                    </div>
+
+                    <!-- #913: Listening Mix — a playable track mix from those recommended artists -->
+                    <div class="discover-section" style="display: none;">
+                        <div class="discover-section-header">
+                            <div>
+                                <h2 class="discover-section-title">🎧 Your Listening Mix</h2>
+                                <p class="discover-section-subtitle">A fresh playlist of tracks from artists matched to your listening</p>
+                            </div>
+                            <div class="discover-section-actions">
+                                <button class="action-button secondary"
+                                    onclick="openDownloadModalForDiscoverPlaylist('listening_mix', 'Your Listening Mix')"
+                                    title="Download missing tracks">
+                                    <span class="button-icon">↓</span><span class="button-text">Download</span>
+                                </button>
+                                <button class="action-button primary" id="listening-mix-sync-btn"
+                                    onclick="startDiscoverPlaylistSync('listening_mix', 'Your Listening Mix')"
+                                    title="Sync to media server">
+                                    <span class="button-icon">⟳</span><span class="button-text">Sync</span>
+                                </button>
+                            </div>
+                        </div>
+                        <div class="discover-sync-status" id="listening-mix-sync-status" style="display: none;">
+                            <div class="sync-status-content">
+                                <div class="sync-status-label"><span class="sync-icon">⟳</span><span>Syncing to media server...</span></div>
+                                <div class="sync-status-stats">
+                                    <span class="sync-stat">✓ <span id="listening-mix-sync-completed">0</span></span>
+                                    <span class="sync-stat">⏳ <span id="listening-mix-sync-pending">0</span></span>
+                                    <span class="sync-stat">✗ <span id="listening-mix-sync-failed">0</span></span>
+                                    <span class="sync-stat">(<span id="listening-mix-sync-percentage">0</span>%)</span>
+                                </div>
+                            </div>
+                        </div>
+                        <div class="discover-playlist-container compact" id="personalized-listening-mix"></div>
+                    </div>
+
                     <div class="discover-section" id="recommended-artists-section" style="display: none;">
                         <div class="discover-section-header">
                             <div>

webui/static/discover.js

@@ -19,6 +19,7 @@ let personalizedPopularPicks = [];
 let personalizedHiddenGems = [];
 let personalizedDailyMixes = [];
 let personalizedDiscoveryShuffle = [];
+let personalizedListeningMix = [];   // #913: the "Your Listening Mix" track playlist
 let buildPlaylistSelectedArtists = [];
 
 async function loadDiscoverPage() {
@@ -27,6 +28,8 @@ async function loadDiscoverPage() {
     // Load all sections
     await Promise.all([
         loadDiscoverHero(),
+        loadListeningRecommendations(),  // #913: play-weighted, consensus-ranked picks
+        loadPersonalizedListeningMix(),  // #913: playable track mix from those picks
         loadRecommendedArtistsSection(),
         loadYourArtists(),
         loadYourAlbums(),
@@ -676,10 +679,28 @@ async function addAllRecommendedToWatchlist(btn) {
 // machinery, so the inline carousel and the "View All" modal stay in sync.
 let _recommendedSectionCtrl = null;
 
-function _renderRecommendedMini(artist, source) {
+// "Because you listen to X, Y" — the listening-driven (#913) variant of the reason line.
+function _listeningRecommendationReason(artist) {
+    const names = (artist && artist.because) || [];
+    if (names.length === 1) return `Because you listen to ${escapeHtml(names[0])}`;
+    if (names.length === 2) return `Because you listen to ${escapeHtml(names[0])} & ${escapeHtml(names[1])}`;
+    if (names.length >= 3) {
+        const shown = names.slice(0, 2).map(escapeHtml).join(', ');
+        return `Because you listen to ${shown} +${names.length - 2} more`;
+    }
+    return 'From artists you play often';
+}
+function _listeningRecommendationReasonTitle(artist) {
+    const names = (artist && artist.because) || [];
+    return names.length ? `You listen to: ${names.join(', ')}` : '';
+}
+
+function _renderRecommendedMini(artist, source, opts) {
+    const reasonFn = (opts && opts.reasonFn) || _recommendationReason;
+    const titleFn = (opts && opts.titleFn) || _recommendationReasonTitle;
     const artistSource = artist.source || source || '';
-    const reason = _recommendationReason(artist);
+    const reason = reasonFn(artist);
-    const reasonTitle = _recommendationReasonTitle(artist);
+    const reasonTitle = titleFn(artist);
     const genreTags = (artist.genres || []).slice(0, 2).map(g =>
         `<span class="recommended-card-genre">${escapeHtml(g)}</span>`
     ).join('');
@@ -712,7 +733,7 @@ function _renderRecommendedMini(artist, source) {
 // Progressively fill in images for the cards we actually rendered (the API
 // returns cached images only; the rest are fetched on demand — same endpoint
 // the modal uses).
-async function _enrichRecommendedCarouselCards(items, source) {
+async function _enrichRecommendedCarouselCards(items, source, carouselId) {
     const idKey = source === 'spotify' ? 'spotify_artist_id'
                 : source === 'deezer' ? 'deezer_artist_id'
                 : 'itunes_artist_id';
@@ -726,7 +747,7 @@ async function _enrichRecommendedCarouselCards(items, source) {
         });
         const data = await resp.json();
         if (!data.success || !data.artists) return;
-        const carousel = document.getElementById('recommended-artists-carousel');
+        const carousel = document.getElementById(carouselId || 'recommended-artists-carousel');
         if (!carousel) return;
         for (const [aid, info] of Object.entries(data.artists)) {
             if (!info.image_url) continue;
@@ -778,6 +799,48 @@ async function loadRecommendedArtistsSection() {
     return _recommendedSectionCtrl.load();
 }
 
+// #913: "Based On Your Listening" — play-weighted, consensus-ranked recommendations.
+// Mirrors loadRecommendedArtistsSection but reads the listening-driven endpoint and
+// renders a "Because you listen to X" reason. Hides itself when empty (no scan yet).
+let _listeningRecsCtrl = null;
+async function loadListeningRecommendations() {
+    if (!_listeningRecsCtrl) {
+        _listeningRecsCtrl = createDiscoverSectionController({
+            id: 'listening-recs',
+            sectionEl: '#listening-recs-section',
+            contentEl: '#listening-recs-carousel',
+            fetchUrl: '/api/discover/listening-recommendations',
+            extractItems: (data) => data.artists || [],
+            isEmpty: (items) => items.length === 0,
+            hideWhenEmpty: true,
+            renderItems: (items, data) => {
+                const source = data.source || 'spotify';
+                const shown = items.slice(0, 18);
+                return shown.map(a => _renderRecommendedMini(a, source, {
+                    reasonFn: _listeningRecommendationReason,
+                    titleFn: _listeningRecommendationReasonTitle,
+                })).join('');
+            },
+            onRendered: ({ data }) => {
+                const carousel = document.getElementById('listening-recs-carousel');
+                if (carousel && !carousel._recoWired) {
+                    carousel._recoWired = true;
+                    carousel.addEventListener('click', function (e) {
+                        const btn = e.target.closest('.recommended-card-watchlist-btn');
+                        if (btn) { e.preventDefault(); e.stopPropagation(); toggleRecommendedWatchlist(btn); }
+                    });
+                }
+                const source = (data && data.source) || 'spotify';
+                _enrichRecommendedCarouselCards((data && data.artists || []).slice(0, 18), source, 'listening-recs-carousel');
+            },
+            loadingMessage: 'Reading your listening...',
+            emptyMessage: 'Play more music and run a watchlist scan to see picks based on your listening',
+            errorMessage: 'Failed to load listening recommendations',
+        });
+    }
+    return _listeningRecsCtrl.load();
+}
+
 function closeRecommendedArtistsModal() {
     const modal = document.getElementById('recommended-artists-modal');
     if (modal) modal.style.display = 'none';
@@ -4246,6 +4309,32 @@ async function loadPersonalizedHiddenGems() {
     }
 }
 
+// #913: "Your Listening Mix" — a playable track playlist from the artists matched to your
+// listening. Mirrors loadPersonalizedHiddenGems; tracks come pre-shaped from the scan so the
+// row renders + syncs like the others. Hides when empty (no scan / no listening data yet).
+async function loadPersonalizedListeningMix() {
+    try {
+        const container = document.getElementById('personalized-listening-mix');
+        if (!container) return;
+
+        const response = await fetch('/api/discover/personalized/listening-mix');
+        if (!response.ok) return;
+
+        const data = await response.json();
+        if (!data.success || !data.tracks || data.tracks.length === 0) {
+            container.closest('.discover-section').style.display = 'none';
+            return;
+        }
+
+        personalizedListeningMix = data.tracks;
+        renderCompactPlaylist(container, data.tracks);
+        container.closest('.discover-section').style.display = 'block';
+
+    } catch (error) {
+        console.error('Error loading listening mix:', error);
+    }
+}
+
 async function loadPersonalizedDailyMixes() {
     try {
         const container = document.getElementById('daily-mixes-grid');
@@ -4300,7 +4389,6 @@ function renderCompactPlaylist(container, tracks) {
         const durationMin = Math.floor(track.duration_ms / 60000);
         const durationSec = Math.floor((track.duration_ms % 60000) / 1000);
         const duration = `${durationMin}:${durationSec.toString().padStart(2, '0')}`;
-        const artistEsc = (track.artist_name || '').replace(/'/g, "\\'").replace(/"/g, '&quot;');
 
         html += `
             <div class="discover-playlist-track-compact" data-track-index="${index}">
@@ -4314,7 +4402,6 @@ function renderCompactPlaylist(container, tracks) {
                 </div>
                 <div class="track-compact-album">${track.album_name}</div>
                 <div class="track-compact-duration">${duration}</div>
-                <button class="track-compact-block" onclick="event.stopPropagation(); blockDiscoveryArtist('${artistEsc}')" title="Block ${artistEsc} from discovery">✕</button>
             </div>
         `;
     });
@@ -8646,6 +8733,8 @@ async function openDownloadModalForDiscoverPlaylist(playlistType, playlistName)
             tracks = personalizedHiddenGems;
         } else if (playlistType === 'discovery_shuffle') {
             tracks = personalizedDiscoveryShuffle;
+        } else if (playlistType === 'listening_mix') {
+            tracks = personalizedListeningMix;
         } else if (playlistType === 'build_playlist') {
             tracks = buildPlaylistTracks;
         }
@@ -8765,6 +8854,8 @@ async function startDiscoverPlaylistSync(playlistType, playlistName) {
         tracks = personalizedHiddenGems;
     } else if (playlistType === 'discovery_shuffle') {
         tracks = personalizedDiscoveryShuffle;
+    } else if (playlistType === 'listening_mix') {
+        tracks = personalizedListeningMix;
     } else if (playlistType === 'build_playlist') {
         tracks = buildPlaylistTracks;
     }