![]() ![]() They have an objective tastemaking computer filling in all the gaps. So that if it is similar to songs that you like, it will push it into your model of what to recommend to you. It looks at the sound files and classifies it based on what it sounds most like. How does that get out there? Spotify uses an algorithm that will listen to every song. ![]() What happens when you have new music from a new band that isn’t all over blogs. Lastly we have what I think is the most wild thing. Instead Spotify has developed a way to do this without doing it manually. This limited how much music was actually rated and reviewed. ![]() Other streaming sites have tried to do this in the past, but often relied on manual curation. This means real world opinions about music, is guiding the Discover weekly. It knows what music is described in the same way, and which users like that music. This example is pretty straightforward, but it is dragging in all kinds of descriptors. This will be picked up by Spotify and then factored into their model, making it more likely to be recommended to fans of modern folk. Say in a recent review, Fran describes Katie Blount as ‘ modern folk’. So Spotify trawls the internet taking information from blogs, newspapers and anything it can get its hands on. It will read through and essentially highlight anything that describes the music. This is just a fancy way of saying that a computer reads text and understands it. To deal with this problem Spotify has an algorithm that uses Natural Language Programming. So what happens if no one has listened to a song? Then our big matrix has a big old gap in it and which isn’t all that helpful. It’s just a matter of having the most data. And your profile and recommendations are changed forever!Īnyway this database is what they say gives it an edge over other streaming platforms. Just let your friend play Backstreet Boys once. This is the model on which all their other algorithms are applied to further refine your scores. Say Nick and I have more or less the same profile, then they are going to recommend me songs that he listened to and I haven’t, and vice versa.īy doing this with everyone and across all the songs, they create a score for each song for how much they should recommend to each user. So how does this help? Recommendations of new songs will come from other people who listen to similar music. Likewise they can group together songs/artists with others, this time based on the profile of users that listen to the music From this matrix users that have similar profiles of songs to each other can be grouped together. Has this person listened to this song (1) or not listened to this song (0). Then going along the top is all 30 milllion+ songs. Going down one side we have all 250 million+ users. They use us against each other.Īll this listener data ends up in a giant matrix. And this is really their lifeblood of their discovery algorithm. All our darkest secrets and guilty pleasures. Considering that they also have been in the biz for so long, this means they have a lot of data on our listening habits. ![]() They reported in October that they had ~250 million active users. Spotify is the largest music streaming platform. Sound Waves – it listens to the songs themselves.Natural language processing – it reads the internet (i.e.Listener base – what do us users like and what do we not like?.There are three main approaches that Spotify uses: But I think it is worth talking broadly about how it works, because it does a lot more than I thought. So how is it doing this? I promise I will not go into the hard maths and the details of the Artificial Intelligence running under the hood. Customising even how it presents itself, to give the user their bespoke experience. The reason it does so well is that the whole Spotify app is built around learning and improving. On the off-chance that someone else finds this interesting I am going to tell you what I found.ĭiscover often does a really great job at giving me new music. And I have to say I have been pretty surprised and impressed. Since then I stepped off the deep end and have been reading way too much about Discover and how it recommends music to folks. In the process I started to try and figure out how Spotify worked. Last time out I was getting all moany about genre. ![]()
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