bvandeusen
fdd14ef04c
Surface in-library albums/artists the listener doesn't actively spin but is predicted to enjoy, derived from the same similarity + like-weighted candidate engine that powers For-You — rolled up from track scores to album/artist granularity. Built in the daily 3am BuildSystemPlaylists pass, atomic-replaced alongside the system playlists, and read back by /api/home (+ /api/home/index). Cold-start gate: skips generation entirely below 20 distinct unskipped tracks AND 5 distinct artists, so a thin profile ships empty rows rather than near-random tiles. - migration 0034: you_might_like_albums / you_might_like_artists (id+rank, CASCADE, per-user rank index). - playlists/you_might_like.go: cold-start gate + similarity roll-up (sum-of-top-3 aggregation, per-artist album cap, daily-rotating via the same userIDHash jitter as For-You) + atomic-replace persist in the tx. - recommendation/home.go: two new HomePayload sections with read-time cross-section dedup vs Most Played / Rediscover / Last Played, trimmed to 10 each. - api: you_might_like_albums / you_might_like_artists on /api/home and /api/home/index, reusing albumRefFrom / artistRefFromCovered. - tests: pure roll-up/aggregation/cap unit tests + DB-backed gate, sufficiency, and atomic-replace tests (all green vs real Postgres). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
67 lines
2.5 KiB
SQL
67 lines
2.5 KiB
SQL
-- "You might like" Home rows (#790). The daily build computes ranked
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-- album/artist IDs in Go (similarity roll-up + cold-start gate) and
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-- atomic-replaces them here; /api/home reads them back hydrated.
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-- name: CountListeningSignalForUser :one
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-- Cold-start gate. Returns the breadth of the user's real listening:
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-- distinct unskipped tracks played and distinct artists played. The
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-- build skips You-might-like entirely below a threshold (a thin history
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-- yields near-random roll-ups). was_skipped=false so a user spamming
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-- next can't inflate the signal.
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SELECT
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count(DISTINCT pe.track_id)::bigint AS distinct_tracks,
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count(DISTINCT t.artist_id)::bigint AS distinct_artists
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FROM play_events pe
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JOIN tracks t ON t.id = pe.track_id
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WHERE pe.user_id = $1 AND pe.was_skipped = false;
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-- name: DeleteYouMightLikeAlbumsForUser :exec
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DELETE FROM you_might_like_albums WHERE user_id = $1;
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-- name: InsertYouMightLikeAlbum :exec
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INSERT INTO you_might_like_albums (user_id, album_id, rank)
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VALUES ($1, $2, $3);
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-- name: DeleteYouMightLikeArtistsForUser :exec
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DELETE FROM you_might_like_artists WHERE user_id = $1;
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-- name: InsertYouMightLikeArtist :exec
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INSERT INTO you_might_like_artists (user_id, artist_id, rank)
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VALUES ($1, $2, $3);
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-- name: ListYouMightLikeAlbumsForUser :many
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-- Read path. Same projection as ListRediscoverAlbumsForUser so the API
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-- layer reuses albumRefFrom(row.Album, row.ArtistName, …). Ordered by
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-- the rank persisted at build time. Final cross-section dedup (vs Most
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-- Played / Rediscover) and the output cap land in the Go layer
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-- (internal/recommendation/home.go).
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SELECT sqlc.embed(albums), artists.name AS artist_name
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FROM you_might_like_albums yml
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JOIN albums ON albums.id = yml.album_id
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JOIN artists ON artists.id = albums.artist_id
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WHERE yml.user_id = $1
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ORDER BY yml.rank
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LIMIT $2;
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-- name: ListYouMightLikeArtistsForUser :many
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-- Read path. Same projection as ListRediscoverArtistsForUser (embeds the
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-- artist + a representative cover_album_id + album_count) so the API
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-- layer reuses artistRefFromCovered. Ordered by persisted rank.
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SELECT sqlc.embed(artists),
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cov.id AS cover_album_id,
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cnt.album_count::bigint AS album_count
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FROM you_might_like_artists yml
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JOIN artists ON artists.id = yml.artist_id
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LEFT JOIN LATERAL (
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SELECT id FROM albums
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WHERE artist_id = artists.id AND cover_art_path IS NOT NULL
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ORDER BY created_at DESC LIMIT 1
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) cov ON true
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LEFT JOIN LATERAL (
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SELECT count(*) AS album_count
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FROM albums WHERE artist_id = artists.id
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) cnt ON true
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WHERE yml.user_id = $1
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ORDER BY yml.rank
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LIMIT $2;
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