Despite the hate it gets from musicians over the less-than-ideal music streaming rates, Spotify is here to stay. Hundreds of millions of people around the world use Spotify to listen to their music, and its unsurprising to see why. With an impressive catalog of over 50 million songs and podcast episodes (and 40,000 new ones being uploaded per day), the Swedish company shows no signs of slowing down.
Besides its attractive monthly price, Spotify listeners are most generally sustained by the platform’s diverse streaming options which include hand-picked songs, full album listens, personal playlists, and virtual radio stations.
With daily and weekly personalized recommendations that are occasionally too on the nose, many people start to wonder how exactly Spotify’s programs and algorithms function.
Below, I’ll dive deep into the inner workings of Spotify to highlight how the company is changing the way many people interact with music through highly intelligent information systems.
Spotify’s “Discover Weekly” playlists are one of the seminal consistencies with the platform’s meteoric rise. Every Monday morning, Spotify users can rely on 30 new songs curated for their personal enjoyment.
For Spotify’s Discover Weekly playlists, daily mixes, and personal recommendations, the software uses a complicated set of algorithms that leverage big data, AI, and machine learning.
More than anything else, Spotify analyzes your streaming history, “liked” songs, “followed” artists, and personal playlists to create the best possible ongoing listening experience.
With this, Spotify can essentially imitate your music-nerd friend who is never shy to give you new album suggestions.
Although they are constantly evolving, we will outline a few of the basic principles of typical Spotify algorithms below. For a wider look at how technology is affecting the industry, feel free to read Jim Ivin’s full editorial on “AI and the Future of Music”.
The primary AI function that Spotify uses to recommend new music is known as collaborative filtering. Collaborative filtering is a smart system that is also used by Netflix and online retailers to suggest relevant content to users based on their previous actions.
Before suggesting new artists and songs to you personally, Spotify creates a back-end “personal taste profile” based on your listening habits. Seamlessly, the algorithm crunches your taste into the entire music landscape and recommends songs that you may like to hear, but have not streamed yet.
The results are rather simple here. If you listen to the Rolling Stones, you will probably recommend a few Classic Rock playlists. However, as you continue to use the platform, your personal music profile becomes more individualized with each stream so that no two Spotify users are exactly alike.
Secondly, Spotify uses Natural Language Processing (NLP) to analyze music and classify each song within the perspective of the music industry as a whole.
To accomplish this, NLP technology is able to scan a song’s metadata (i.e. artist, album, genre, release date, lyrics, etc.) as well as its cultural influence through blogs, shares, mentions, and more across the web.
Once the scan is complete, Spotify then ranks songs as they identify with specific cultural keywords. For instance, consider the song “We Will Rock You” by Queen. Although Freddy Mercury was never famously an athlete, the song is featured on many of Spotify’s sports-related playlists thanks to its powerful lyrics and popularity in stadiums, movies, and more.
Much like in facial recognition technology, Spotify users Conventional Neural Networks (CNN) to quickly and efficiently analyze a piece of music. Using only the audio waveform, Spotify is able to instantly learn and report every song’s tempo, key, volume, and more.
After assigning mathematical values to the song’s tone, pitch, mood, and sound profile, Spotify can then recommend successive songs that fit similar models. In simple terms, this helps separate the pump-up songs from the chill-out tunes while your music is auto-playing.
Thanks to CNN’s, independent artists may be able to compete with the big names with the help of Spotify’s unbiased audio analysis. For instance, if you spend your waking hours listening to the songwriting classics of John Prine or Bob Dylan, an emerging folk artist may land themselves on your Discover Weekly playlist if their songs resemble the neural network of the American classics.
Spotify’s ever-present algorithms never take a day off as they meticulously curate personal playlists for millions of users. In fact, they are getting better at what they do with constant testing, analysis, and machine learning functions put into place.
On both sides of the coin, Spotify’s machine learning is designed to help its users. For music producers, Spotify’s acquisition of Mediachain Labs is a large step to ensure that musicians are paid for each stream. With a better understanding of their listeners, bands and artists may even be able to leverage Spotify’s AI to get to know their audience and produce the music and events that they crave.
If you’ve linked your Spotify account with your Facebook, well then, it is safe to say that the company may know more about you than you may be comfortable with. This includes your gender, age, interests, occupation, and more (not to mention your affinity for Meatloaf’s Bat Out of Hell).
For those who have signed up for Spotify without forfeiting much personal information, the company can only make assumptions about you based on your listening habits and location. In addition to music recommendations, for non-premium users, this may result in a more personalized ad experience between customized music recommendations.
Spotify’s intelligent algorithms make it incredibly easy to discover and listen to new music. Although it can occasionally feel like the platform is really trying to convince you to listen to certain artists or songs, the smart recommendation system can help broaden musical horizons for practically any user.
Of course, we do not think that the “human factor” will ever die in the music industry. Spotify knows this, and they allow users and artists to create their own personal playlists with all of the intricacies and choices that are only possible with the human touch.