Discover Weekly, your weekly mixtape of fresh music, is celebrating an anniversary—five years of discovery this July. But the beloved playlist has humble origins, initially starting as an idea at Spotify’s annual Hack Week. Since then, it’s become known as one of our flagship offerings, was parodied on April Fools (2019’s Disco Cover Weekly, anyone?), and has introduced our Spotify users to some of their new favorite artists.
Skip past songs you don’t like. If you skip a song before it hits the 30-second mark, Spotify registers. Discover Weekly. The playlist made just for you, every Monday. GET SPOTIFY FREE Get playlist To generate your Discover Weekly playlist - we need to get to know your tastes for a few weeks, so get Spotify. As more people listen to your music and add it to their playlists, it’s more likely to appear in Discover Weekly. Here’s how Discover Weekly works. Music fans on Spotify have created over 2 billion playlists. In July 2015, popular music streaming service Spotify launched a feature which was – at the time – pretty uncommon: Discover Weekly. The concept was simple. Spotify’s Discover Weekly recommendation model isn’t a revolutionary one. Instead, it’s a combination of a number of effective recommendation techniques previously used by other industry.
In the five years since its launch, listeners have also streamed endless hours of the Discover Weekly playlist—over 2.3 billion hours between July 2015 and June 25, 2020 For the numerically inclined, that’s more than:
For context: That’s longer than human civilization has been around!
Weekly Discovery Spotify
Since the playlist updates every Monday with new music based on your personal listening habits, it leads users to find new artists, tracks, and hits to fall in love with. The Moroccan-Dutch DJ R3HAB is the most “discovered” artist in the most markets—16, to be exact—meaning users across those countries streamed him the most out of any other creator on their Discover Weekly playlist.
“I love how Spotify allows my music to connect with people across so many cultures,” R3HAB told For the Record. “I’ve always considered myself a world artist and it’s amazing to see my music truly traveling. Spotify has broken down the geographical boundaries of music discovery, allowing people from all over to discover my music as soon as it’s released. Thank you, Discover Weekly.”
Halsey takes the spot for most discovered female artist globally. Notably, she released her first album, Badlands, in 2015—the same year Discover Weekly was created! Now, she’s included on the list of Top Streamed Female Artists on Spotify.
Can you download spotify songs off wifi. U.S. listeners stream Discover Weekly the most, and have spent a lot of time “discovering” music from RAC and Khalid. In the U.K. listeners have found Detroit-born house producer MK, and German DJ Alle Farben racks up the streams in his home country. And they’re discovering artists across genres too—everything from EDM to grupera (a regional Mexican style), to indietronica and Lithuanian folk.
With so much discovery in such a short span of time, (you know, compared to the entirety of human civilization), we can’t wait to see what the next five years will bring for this star playlist.
New music is everywhere. Hundreds if not thousands of new albums are released each week between major labels, mid-level subsidiaries, independent shops, and droves of label-less hopefuls. So with all those sweet new tunes out there, how do you dig through the dreck and find what sings to your soul?
Music is a deeply personal experience, and describing what you like or dislike about a particular song or artist can sometimes be frustratingly difficult. This can make finding new music difficult, and discovering hidden gems near impossible.
The answer? Spotify Discover Weekly. As veteran Spotify users know, Discover Weekly is a curated playlist of 30 songs ranging from new releases to deep cuts, personalized just for you. But how does it work? Data science.
“Recommendation is a really common problem for data scientists,” said Lucas Ramadan, a student in Galvanize’s data science program. “The most common technique used for recommendation is called collaborative filtering.”
Recommendation engines have become commonplace in our daily lives. Netflix uses them to recommend new movies and TV shows we might like, while Amazon uses them to turn shoppers on to new products. The trick to collaborative filtering is that it recommends new things based on similarity between users, not between items.
In the case of Spotify, that means a huge database filled with everything that users have already listened to, where the rows are filled with users, and the columns are all the songs each user has listened to. A collaborative filtering algorithm finds users that are similar to each other, based upon their usage—the songs in common they have listened to—and then recommends the songs that only one person has listened to to the other.
Spotify Discover Weekly Bad
But collaborative filtering isn’t the only thing responsible for setting you up with that hot new M83 track. Spotify discover actually uses what’s known as an ensemble method—a collection of models of which collaborative filtering is a member of.
“A big problem for collaborative filtering is what’s called the ‘cold start problem,’ which is when you’re starting a new product and you have no user data,” Ramadan said. For Spotify, this manifests when you have a new user who hasn’t listened to very much yet, as well as when you have an obscure, unpopular, or new song that not many people have listened to yet.
Spotify Discover Weekly History
https://everdc633.weebly.com/blog/if-i-download-music-from-spotify-will-it-use-data. Spotify wants to be able to recommend these new songs (and deep cuts) so to get around the cold start problem, it uses what’s called convolutional neural networks to actually analyze the songs themselves.
“The convolutional neural network is run over the acoustics of a song itself and analyzed to determine songs that have similar acoustic patterns,” Ramadan said.
A third method used is a form of natural language processing. In natural language processing, there’s a technique called Word2Vec, which takes words and encodes them into a mathematical representation—a vector. In these mathematical representations, vectors with a similar shape would equate to words with a similar meaning. Basically, it’s mathematical representation of the implicit associations and relationships between words that we know to be true in everyday speech.
What Spotify does is very similar to Word2Vec. It takes playlists and treats them as a paragraph or big block of text, and treats each song in the playlist as an individual word. This results in vector representations of songs that can be used to determine two pieces of music that are similar. As such, Spotify is able to determine which songs are similar to each other, thus enabling it to tackle the cold start problem and recommend songs with very few plays.
Discover Weekly Spotify Free Download
One of the things that makes Discover so good is that it employs a technique called outlier detection to differentiate things you actually like. Outlier detection is commonly used in financial security—it’s what banks and credit card companies use to detect fraudulent charges—but it also has uses in recommendation engines.
Essentially, outlier detection is used to determine if a particular usage—that is, listening to a song—is part of a normal pattern of behavior or not. This way, if you usually only listen to classic rock and ’90s alternative, your Discover Weekly playlist won’t get filled up with pop hits when your little brother plays Justin Bieber one time.
Spotify Last Week Discover Weekly
“Now, if you keep listening to Bieber 50-50 with other stuff, then it will start to recommend songs similar to Bieber,” Ramadan said. “The idea is that it initially flags it as an outlier and largely ignore it, only adding it to your recommendations if you continue that usage pattern.” Samsung watch download spotify.
Discover Weekly Spotify Free App
With all these algorithms working together, it’s no wonder that Discover Weekly is a hit. The general sentiment seen on places such as Twitter, as well as feedback collected by Spotify itself, suggests that people are very pleased with the 30 new songs recommended each week.
And if not? Well, all you can blame is the data.
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