“…it’s your daylist, if you only believe*…” in Spotify
Out of all the subscription music services available–and there are a lot of them–I’ve stuck with Spotify, mostly because at the time I was trying to settle on one, it worked the best with all of my devices, home speakers, etc. and I haven’t really regretted the choice (it even came bundled with a Hulu subscription).
It’s been fascinating to watch them leverage more and more data both from the large collection of tracks in their library, but also from my listening habits. Also amusing when they’ve gotten things wrong, and honestly continue to get things not-quite-right when I want to listen to “songs like” a certain one, or a particular genre.
Honestly, genre-matching has been one of the hardest things for me on any music service, since everyone’s idea of genres are different and personal and we can only rely on how the music is submitted to their database, for the most part. So I’m content and, I suspect, most of us have been with what Spotify does on a regular basis while just looking forward to our end-of-year Spotify Wrapped. (2023’s is coming soon!)
While it’s always fun to see and share our listening habits, this past September on my birthday–you’re welcome–they introduced daylist, an ever-changing personalized playlist serving you a selection of what you might usually listen to for 4-hour periods throughout the day.
This new, one-of-a-kind playlist on Spotify ebbs and flows with unique vibes, bringing together the niche music and microgenres you usually listen to during particular moments in the day or on specific days of the week. It updates frequently between sunup and sundown with a series of highly specific playlists made for every version of you. It’s hyper-personalized, dynamic, and playful as it reflects what you want to be listening to right now.—Spotify
I confess that I hadn’t started adding “micro-” to things like “genre” or “influencers” until recent years, but I will admit that sometimes the daylist is pitch perfect. Except… how can it not be? I’ve fed it everything it needs to know!
I do get ready for the day with House music, I totally listen to Lo-Fi while trying to work, and it even remembered the one afternoon I was super into City Pop. But I wonder… am I feeding back into my own music recommendation algorithms by listening to the daylist?
Same thoughts about Spotify Wrapped, because Spotify encourages you to listen to your faves, making a playlist from them. But by doing so, am I front-loading the algorithm with those same tracks again, or does it ignore late December/early January listening. I suppose it must since I’ve never had it tell me about my “holiday” listening habits, so it must separate out seasonal tunes.
And I can see that it’s still evolving. The specific “day of the week” word comes and goes from my daylist, so they may be adjusting that part of its generated description. It has the same feel as “Wrapped” because it’s easy to capture and share. I’ve already seen “Don’t tell me your astrology sign, search daylist on Spotify and give me the title.” floating around Twitter/X.
But there’s also an ICYMI or carrot-and-stick aspect to it. You can save the daylist to your favorite playlists for easy access, but you can’t save the particular 4-hour curated playlist (unless you specifically copy every song on it into a new separate playlist). You can’t tell the daylist to “keep going” past its allotted time, you’d have to create a new “songs like this” radio station from a track on it.
At the end of the day, or at least the end of each 4-hour period, I think it’s cool, and anything that gets me listening to different music is good, but I can’t help wondering if I’m just starting an eternal feedback loop of, “You usually listen to this around now, so we’ll play it for you. Which means that, around now, you’re confirming that you listen to this type of music.” and so on.
So I’ve been actively trying to choose and listen to different things, sometimes completely left-field genres than the current daylist to see what happens. But I am hard-pressed to admit that when it’s right? It’s right.
Do I wholeheartedly welcome our new algorithmic machine learning/large language model overlords? Not just yet… but I will let them DJ for me… for now. 😏