How Spotify’s Algorithm Works
How does Spotify always seem to know what you want to hear next? Explore the secrets behind its powerful recommendation algorithm!
Spotify is one of the most popular ways in which we discover music, with its algorithm being one of the most advanced in the streaming industry. Whether it’s your personalized Discover Weekly, the Daily Mixes, or curated Release Radar, the platform continuously tailors recommendations based on your listening habits. But how exactly does Spotify’s algorithm work? Let’s break it down.
Collaborative filtering
Spotify uses collaborative filtering, a machine learning technique that analyzes your behavior and compares it with other users who have similar tastes. It considers factors like:
- Songs you frequently play
- Artists you follow
- Playlists you create
- Tracks you save or like
- Songs you skip
By recognizing patterns in your listening habits, Spotify groups you with users who share similar music preferences. This allows it to suggest songs that you might like based on what others with similar tastes enjoy—even if you’ve never heard them before.
Natural language processing (NLP)
Spotify doesn’t just analyze what you listen to—it also scans the internet for how music is being talked about. Using Natural Language Processing (NLP), Spotify gathers information from:
- Blog posts and music reviews
- News articles
- Social media discussions
- Online playlists and metadata
This helps the algorithm determine which songs are trending, how artists are described, and how different tracks are related. For example, if a new indie artist is gaining popularity in online discussions, Spotify may begin recommending them to listeners of similar genres, which is why it’s important to try and spend some time putting together a good PR campaign before launching a release.
Audio analysis
Beyond tracking user behavior and online trends, Spotify’s algorithm also analyzes the raw audio of songs. It examines specific features of each track, including:
- Tempo
- Key & pitch
- Energy level
- Danceability
By breaking music down into measurable characteristics, Spotify can recommend songs that “sound” similar, even if they come from different genres or artists. This explains why a song you’ve never heard before might fit perfectly into one of your playlists.
Algotorial playlists
One of Spotify’s biggest strengths is its balance between algorithm-driven recommendations and human curation. This is called “algotorial” curation, where Spotify’s AI works alongside professional playlist editors.
For example:
- Discover Weekly is entirely algorithm-driven, personalized based on your listening data.
- RapCaviar, Today’s Top Hits, and New Music Friday are curated by human editors but use Spotify’s data insights to select trending tracks.
By combining human intuition with AI-generated suggestions, Spotify creates playlists that feel both personalized and culturally relevant.
Feedback loops
Spotify’s algorithm is constantly learning. Every action you take provides feedback that refines its recommendations. Some key behaviors that influence your suggestions include:
- Liking a song → More recommendations similar to that track.
- Skipping a song → Fewer recommendations like that in the future.
- Adding a song to a playlist → More songs that match that playlist’s vibe.
- Listening to a song on repeat → Signals that you love the track, leading to similar recommendations.
Spotify even considers listening duration—if you skip a song after 10 seconds, it assumes you didn’t like it. If you play a song all the way through, it assumes it was a positive experience.
Release radar
Spotify’s Release Radar is another personalized playlist, but instead of recommending songs based on your past listening, it prioritizes new releases from artists you follow or frequently listen to.
If you interact with a certain artist often, their latest releases will likely appear in your Release Radar. This feature ensures that fans stay up to date with their favorite musicians.
