Spotify recommendation algorithm: understand it with real data
🎧 Setting the context
Have you ever stopped to think about how the Spotify recommendation algorithm works? If you use the app often, you have probably noticed that playlists like Discover Weekly and Release Radar seem to guess exactly what you want to hear.
In practice, that is not magic. It is the result of a powerful combination of data analysis and artificial intelligence. Spotify interprets your listening habits to deliver suggestions that become more accurate over time.
As a data analyst, I decided to turn that curiosity into a practical experiment. I collected my own usage data from the platform and built an interactive dashboard from it.
🎵 How does Spotify understand your musical taste?
The Spotify recommendation algorithm uses artificial intelligence to analyze your behavior and suggest songs that match your profile. To do that, it evaluates several technical characteristics of the tracks you listen to, like, and save to playlists.
These characteristics receive values between 0 and 1, representing the intensity of each aspect in a song. The closer the score is to 1, the stronger that characteristic is. Here are a few examples:
- 🎸 Energy: Measures agitation and intensity, such as volume, speed, and timbre. Rock and metal usually score between 0.8 and 1.0, while classical music tends to stay below 0.3.
- 💃 Danceability: Indicates whether a song is suitable for dancing based on rhythm and tempo. Pop and funk often score above 0.7.
- 🎤 Instrumentalness: Estimates the probability of a track being instrumental. Soundtracks score high, while rap and country music tend to score low because of prominent vocals.
- 👥 Liveness: Detects the presence of an audience in the recording. Live versions are often above 0.8, while studio versions usually fall between 0.1 and 0.3.
📌 And what does Spotify do with all of this?
The Spotify recommendation algorithm crosses this information with the history of other users who have similar tastes. On top of that, it uses machine learning and text analysis techniques to enrich the suggestions even more.
Based on those signals, the platform assembles personalized playlists and recommends songs with technical characteristics similar to the ones you consume the most. That is why many recommendations feel so accurate.
📊 Explore the dashboard
Using my Spotify history since 2018, I built a complete dashboard in Power BI. Visualizing the data helped me identify several interesting patterns over time:
- How my musical taste evolved over the years.
- Most-played tracks and seasonality by time of year or week.
- A heat map showing the hours and days with the highest activity.
- Analysis of average intensity and variation of attributes such as Danceability and Energy over time.
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