[FAQ]

A question that comes to mind for me is about Spotify’s ‘magic’:

How does Spotify’s algorithm actually work when it comes to recommending music and creating personalised playlists like ‘Discover Weekly’ for new listeners in 2025?

I’m fascinated by how it can take a new user and quickly build a profile of their tastes. What are the main signals it looks at? Is it just the artists you follow, or does it go deeper into the characteristics of the music itself?

Curious about the tech behind it. Thanks!

[Jeff Bullas]

That is a great question. The ‘magic’ of Spotify’s recommendation engine, which powers its personalised playlists like Discover Weekly, is a combination of a few different powerful technologies working together.

The system’s goal is to keep you listening by understanding your taste on a deep level, and it does this in three main ways.

First, it uses a model called Collaborative Filtering. This system does not analyse the music itself, but rather your listening habits. It looks at what you play, save, and add to playlists, and then it finds other users across the globe who have very similar tastes. It then recommends music to you that those other users listen to but that you have not yet heard. It is based on the powerful idea that “people like you also liked this.”

Second, it uses Content-Based Filtering. This is where Spotify’s systems actually analyse the raw audio files of the songs in its catalogue. They classify music based on dozens of characteristics like tempo, key, energy level, “danceability,” and overall mood. This allows the algorithm to find and recommend songs that sound sonically similar to what you already enjoy, even from artists you have never discovered before.

And third, it uses Natural Language Processing. This system constantly scans the internet, looking at what people are writing about certain artists and songs in blog posts, news articles, and on social media. This helps the algorithm understand the context, the cultural conversation, and the sentiment surrounding the music, and it learns which artists are often talked about together.

All of this is powered by your actions. Every time you ‘like’ a song, skip a song before it finishes, add a track to a playlist, or listen to a song all the way through, you are providing a new data point that refines your personal taste profile.

In summary, Spotify recommends music by combining these three models: analysing your habits against similar users, analysing the sonic properties of the music itself, and analysing the cultural conversation happening online. It is a powerful system for discovery that learns more about you with every song you play.

Cheers,

Jeff

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