One algorithm that I really enjoy is the daily music recommendations on Netease music, and I knew its development trajectory really well. The recommendation algorithm was first introduced into the music app about 6-7 years ago, and only have a feature of "daily recommendation" that contains 30 songs based on the songs the users listen and added to playlists before. It wasn't a big feature and was not so advanced back then, but the algorithm was rapidly evolved and start to hit more and more people's sweet spot by assesing both short term data (like the songs the user listened to the day before) and long term data (like the song genres the user is interested in this month), and adding more accurate and precise labels to songs. Sometimes the labels are so precise that the algorithms can deduce the game the songs I'm listening to come from and recommend other pieces from the same game.
About 2 years ago, the algorithm experiences another great upgrade and start to have sub-category recommendations. For example, there are classic and J-pop recommendations that only provides these kinds of pieces based on my interest, yet the effectiveness of the recommendation is still top-tier.
It's not an exagerration to say that the recommendation algorithm of Netease music perfectly addresses the pain of finding new songs that fit my taste. I also really appreciate the data privacy of the algorithm, since it does not ask for privacy information like location, contacts, etc. Even a newly established account with no info can use the algorithm.