bvandeusen
277898a49a
Add per-user artist-suggestion service ranking out-of-library MBIDs by signal x similarity. Single-CTE SQL collects user likes (5x weight) and recency-decayed plays, joins against artist_similarity_unmatched, and filters in-library candidates plus non-terminal lidarr_requests. The service resolves top-3 attribution seeds to artist names in a batched GetArtistsByIDs call so the UI can render "because you liked X" reasons. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
204 lines
7.5 KiB
SQL
204 lines
7.5 KiB
SQL
-- name: LoadRadioCandidates :many
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-- Returns all tracks except the seed and any played by the user within
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-- the last $3 hours, joined with the stats needed for scoring:
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-- is_liked — boolean from general_likes for this user
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-- last_played_at — max(play_events.started_at) for this user/track
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-- play_count — count of play_events for this user/track
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-- skip_count — play_events where was_skipped=true
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SELECT
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sqlc.embed(t),
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(l.user_id IS NOT NULL)::bool AS is_liked,
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pe.last_played_at::timestamptz AS last_played_at,
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pe.play_count,
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pe.skip_count
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FROM tracks t
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LEFT JOIN general_likes l ON l.user_id = $1 AND l.track_id = t.id
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LEFT JOIN LATERAL (
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SELECT
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max(started_at) AS last_played_at,
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count(*) AS play_count,
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count(*) FILTER (WHERE was_skipped) AS skip_count
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FROM play_events
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WHERE user_id = $1 AND track_id = t.id
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) pe ON true
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WHERE t.id <> $2
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AND NOT EXISTS (
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SELECT 1 FROM play_events
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WHERE user_id = $1 AND track_id = t.id
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AND started_at > now() - $3 * interval '1 hour'
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)
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AND NOT EXISTS (
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SELECT 1 FROM lidarr_quarantine q
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WHERE q.user_id = $1 AND q.track_id = t.id
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);
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-- name: LoadRadioCandidatesV2 :many
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-- M4c: similarity-driven candidate pool. 5-way UNION:
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-- $1 user_id, $2 seed_track_id, $3 recently_played_hours,
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-- $4 exclude (uuid[]), $5 lb_similar K, $6 similar_artists K,
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-- $7 tag_overlap K, $8 likes_overlap K, $9 random_fill K.
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-- Returns same shape as LoadRadioCandidates plus similarity_score column.
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WITH
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seed_artist AS (
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SELECT artist_id
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FROM tracks
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WHERE tracks.id = $2
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),
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seed_tags AS (
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SELECT trim(g) AS tag
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FROM tracks t
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LEFT JOIN LATERAL regexp_split_to_table(coalesce(t.genre, ''), '[;,]') AS g(tag) ON true
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WHERE t.id = $2 AND trim(g) <> ''
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),
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excluded_ids AS (
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SELECT unnest($4::uuid[]) AS id
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UNION ALL
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SELECT pe.track_id AS id
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FROM play_events pe
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WHERE pe.user_id = $1 AND pe.started_at > now() - $3 * interval '1 hour'
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UNION ALL
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SELECT q.track_id AS id
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FROM lidarr_quarantine q
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WHERE q.user_id = $1
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),
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lb_similar AS (
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SELECT ts.track_b_id AS track_id, ts.score AS sim_score
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FROM track_similarity ts
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WHERE ts.track_a_id = $2
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AND ts.source = 'listenbrainz'
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AND ts.track_b_id NOT IN (SELECT id FROM excluded_ids)
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ORDER BY ts.score DESC
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LIMIT $5
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),
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similar_artists AS (
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SELECT t.id AS track_id, asim.score * 0.5 AS sim_score
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FROM artist_similarity asim
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JOIN tracks t ON t.artist_id = asim.artist_b_id
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JOIN seed_artist sa ON asim.artist_a_id = sa.artist_id
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WHERE asim.source = 'listenbrainz'
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AND t.id NOT IN (SELECT id FROM excluded_ids)
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ORDER BY asim.score DESC, random()
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LIMIT $6
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),
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tag_overlap AS (
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SELECT t.id AS track_id,
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(count(DISTINCT trim(g))::float8
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/ GREATEST((SELECT count(*) FROM seed_tags), 1)) AS sim_score
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FROM tracks t
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LEFT JOIN LATERAL regexp_split_to_table(coalesce(t.genre, ''), '[;,]') AS g_split(g) ON true
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WHERE trim(g_split.g) IN (SELECT tag FROM seed_tags)
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AND t.id NOT IN (SELECT id FROM excluded_ids)
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AND t.id <> $2
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GROUP BY t.id
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HAVING count(DISTINCT trim(g_split.g)) > 0
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ORDER BY sim_score DESC
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LIMIT $7
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),
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likes_overlap AS (
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SELECT gl.track_id, 0.6::float8 AS sim_score
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FROM general_likes gl
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WHERE gl.user_id = $1
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AND gl.track_id NOT IN (SELECT id FROM excluded_ids)
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AND EXISTS (
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SELECT 1 FROM tracks t
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LEFT JOIN LATERAL regexp_split_to_table(coalesce(t.genre, ''), '[;,]') AS g_overlap(g) ON true
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WHERE t.id = gl.track_id
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AND trim(g_overlap.g) IN (SELECT tag FROM seed_tags)
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)
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ORDER BY random()
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LIMIT $8
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),
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random_fill AS (
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SELECT t.id AS track_id, 0.0::float8 AS sim_score
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FROM tracks t
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WHERE t.id NOT IN (SELECT id FROM excluded_ids)
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AND t.id <> $2
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AND t.id NOT IN (
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SELECT track_id FROM lb_similar
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UNION SELECT track_id FROM similar_artists
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UNION SELECT track_id FROM tag_overlap
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UNION SELECT track_id FROM likes_overlap
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)
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ORDER BY random()
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LIMIT $9
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)
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SELECT
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sqlc.embed(t),
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(l.user_id IS NOT NULL)::bool AS is_liked,
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pe.last_played_at::timestamptz AS last_played_at,
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pe.play_count,
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pe.skip_count,
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COALESCE(max(u.sim_score), 0.0) AS similarity_score
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FROM (
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SELECT track_id, sim_score FROM lb_similar
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UNION ALL SELECT track_id, sim_score FROM similar_artists
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UNION ALL SELECT track_id, sim_score FROM tag_overlap
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UNION ALL SELECT track_id, sim_score FROM likes_overlap
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UNION ALL SELECT track_id, sim_score FROM random_fill
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) u
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JOIN tracks t ON t.id = u.track_id
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LEFT JOIN general_likes l ON l.user_id = $1 AND l.track_id = t.id
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LEFT JOIN LATERAL (
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SELECT max(started_at) AS last_played_at,
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count(*) AS play_count,
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count(*) FILTER (WHERE was_skipped) AS skip_count
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FROM play_events
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WHERE user_id = $1 AND track_id = t.id
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) pe ON true
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GROUP BY t.id, t.title, t.album_id, t.artist_id, t.duration_ms, t.file_path,
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t.file_format, t.file_size, t.bitrate, t.track_number, t.disc_number,
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t.mbid, t.genre, t.added_at, t.updated_at,
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l.user_id, pe.last_played_at, pe.play_count, pe.skip_count;
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-- name: SuggestArtistsForUser :many
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-- M5c: per-user artist suggestions ranked by signal x similarity. The
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-- seeds CTE collects the user's likes (x5) plus recency-decayed plays
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-- (exp(-age_days / $2)). The contributions CTE joins those seeds against
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-- artist_similarity_unmatched and filters out candidates already in
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-- library or already in a non-terminal lidarr_request. The outer SELECT
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-- aggregates per candidate, returning the top-3 contributing seeds for
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-- attribution. $1=user_id, $2=half_life_days, $3=limit.
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WITH seeds AS (
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SELECT a.id AS artist_id,
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5.0 * (CASE WHEN gla.artist_id IS NOT NULL THEN 1 ELSE 0 END)
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+ COALESCE(SUM(EXP(- EXTRACT(epoch FROM now() - pe.started_at) / ($2::float8 * 86400.0))), 0)
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AS signal,
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(gla.artist_id IS NOT NULL) AS is_liked,
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COUNT(pe.id)::bigint AS play_count
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FROM artists a
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LEFT JOIN general_likes_artists gla ON gla.artist_id = a.id AND gla.user_id = $1
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LEFT JOIN tracks t ON t.artist_id = a.id
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LEFT JOIN play_events pe ON pe.track_id = t.id AND pe.user_id = $1
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WHERE gla.artist_id IS NOT NULL OR pe.id IS NOT NULL
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GROUP BY a.id, gla.artist_id
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),
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contributions AS (
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SELECT u.candidate_mbid,
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u.candidate_name,
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seeds.artist_id AS seed_id,
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seeds.is_liked,
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seeds.play_count,
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seeds.signal * u.score AS contribution
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FROM artist_similarity_unmatched u
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JOIN seeds ON seeds.artist_id = u.seed_artist_id
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WHERE NOT EXISTS (SELECT 1 FROM artists WHERE mbid = u.candidate_mbid)
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AND NOT EXISTS (
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SELECT 1 FROM lidarr_requests r
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WHERE r.user_id = $1
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AND r.lidarr_artist_mbid = u.candidate_mbid
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AND r.status NOT IN ('rejected', 'failed')
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)
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)
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SELECT candidate_mbid,
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candidate_name,
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SUM(contribution)::float8 AS total_score,
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((array_agg(seed_id ORDER BY contribution DESC))[1:3])::uuid[] AS top_seed_ids,
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((array_agg(contribution ORDER BY contribution DESC))[1:3])::float8[] AS top_contributions,
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((array_agg(is_liked ORDER BY contribution DESC))[1:3])::boolean[] AS top_is_liked,
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((array_agg(play_count ORDER BY contribution DESC))[1:3])::bigint[] AS top_play_counts
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FROM contributions
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GROUP BY candidate_mbid, candidate_name
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ORDER BY total_score DESC
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LIMIT $3;
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