feat(recommendation): SuggestArtists service for M5c

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>
This commit is contained in:
2026-05-01 06:20:02 -04:00
parent 5e73f590a9
commit 277898a49a
6 changed files with 619 additions and 0 deletions
+96
View File
@@ -294,3 +294,99 @@ func (q *Queries) LoadRadioCandidatesV2(ctx context.Context, arg LoadRadioCandid
}
return items, nil
}
const suggestArtistsForUser = `-- name: SuggestArtistsForUser :many
WITH seeds AS (
SELECT a.id AS artist_id,
5.0 * (CASE WHEN gla.artist_id IS NOT NULL THEN 1 ELSE 0 END)
+ COALESCE(SUM(EXP(- EXTRACT(epoch FROM now() - pe.started_at) / ($2::float8 * 86400.0))), 0)
AS signal,
(gla.artist_id IS NOT NULL) AS is_liked,
COUNT(pe.id)::bigint AS play_count
FROM artists a
LEFT JOIN general_likes_artists gla ON gla.artist_id = a.id AND gla.user_id = $1
LEFT JOIN tracks t ON t.artist_id = a.id
LEFT JOIN play_events pe ON pe.track_id = t.id AND pe.user_id = $1
WHERE gla.artist_id IS NOT NULL OR pe.id IS NOT NULL
GROUP BY a.id, gla.artist_id
),
contributions AS (
SELECT u.candidate_mbid,
u.candidate_name,
seeds.artist_id AS seed_id,
seeds.is_liked,
seeds.play_count,
seeds.signal * u.score AS contribution
FROM artist_similarity_unmatched u
JOIN seeds ON seeds.artist_id = u.seed_artist_id
WHERE NOT EXISTS (SELECT 1 FROM artists WHERE mbid = u.candidate_mbid)
AND NOT EXISTS (
SELECT 1 FROM lidarr_requests r
WHERE r.user_id = $1
AND r.lidarr_artist_mbid = u.candidate_mbid
AND r.status NOT IN ('rejected', 'failed')
)
)
SELECT candidate_mbid,
candidate_name,
SUM(contribution)::float8 AS total_score,
((array_agg(seed_id ORDER BY contribution DESC))[1:3])::uuid[] AS top_seed_ids,
((array_agg(contribution ORDER BY contribution DESC))[1:3])::float8[] AS top_contributions,
((array_agg(is_liked ORDER BY contribution DESC))[1:3])::boolean[] AS top_is_liked,
((array_agg(play_count ORDER BY contribution DESC))[1:3])::bigint[] AS top_play_counts
FROM contributions
GROUP BY candidate_mbid, candidate_name
ORDER BY total_score DESC
LIMIT $3
`
type SuggestArtistsForUserParams struct {
UserID pgtype.UUID
Column2 float64
Limit int32
}
type SuggestArtistsForUserRow struct {
CandidateMbid string
CandidateName string
TotalScore float64
TopSeedIds []pgtype.UUID
TopContributions []float64
TopIsLiked []bool
TopPlayCounts []int64
}
// M5c: per-user artist suggestions ranked by signal x similarity. The
// seeds CTE collects the user's likes (x5) plus recency-decayed plays
// (exp(-age_days / $2)). The contributions CTE joins those seeds against
// artist_similarity_unmatched and filters out candidates already in
// library or already in a non-terminal lidarr_request. The outer SELECT
// aggregates per candidate, returning the top-3 contributing seeds for
// attribution. $1=user_id, $2=half_life_days, $3=limit.
func (q *Queries) SuggestArtistsForUser(ctx context.Context, arg SuggestArtistsForUserParams) ([]SuggestArtistsForUserRow, error) {
rows, err := q.db.Query(ctx, suggestArtistsForUser, arg.UserID, arg.Column2, arg.Limit)
if err != nil {
return nil, err
}
defer rows.Close()
var items []SuggestArtistsForUserRow
for rows.Next() {
var i SuggestArtistsForUserRow
if err := rows.Scan(
&i.CandidateMbid,
&i.CandidateName,
&i.TotalScore,
&i.TopSeedIds,
&i.TopContributions,
&i.TopIsLiked,
&i.TopPlayCounts,
); err != nil {
return nil, err
}
items = append(items, i)
}
if err := rows.Err(); err != nil {
return nil, err
}
return items, nil
}