I tried to make embeddings from my personal listening history a few years ago (https://github.com/ruuda/deepnote) with the idea that listens of music that matches well would be played in the same session, basically word2vec applied to listening history. It didn't work very well, it mostly found that tracks on albums are similar because I tend to listen to full albums. Maybe I didn't stir the parameters enough, or maybe it needs far more data. (All of Listenbrainz?) I also still want to experiment with generating embeddings for tracks just from the time of the day, week, and year. (I tend to listen to different things on a Friday night vs. Sunday morning, and I listen to very different music in summer vs. winter.) But that's for locally recommending relevant tracks that you already have in your library (for https://github.com/ruuda/musium), not for discovering new music. I already implemented a sorting mode to "rediscover" albums (albums that have a lot of listens in the past but few recently), and it works reasonably well. I expect that adding time of day/week/year will improve this a lot, but I haven't implemented it yet. I wonder how much of an improvement a transformer like in the article adds on top of that.