It's from the book by Carlsson and Leijonhufvud. Perhaps Spotify uses ML today, but the key insight from the book was that no ML was needed to build a recommender system. You can just show people songs from custom playlists curated by powerusers. So when your playlist ends you find other high quality playlists that overlap with the music you just listened to. Then you blend those playlists and enqueue new tracks. This is from memory so I might have gotten the details wrong, but I remember that this approach worked like magic and solved the issues with the ML system (bland or too random recommendations). No reason to use ML when you already have millions of manually curated playlists.