Spotify, the green revolutionary app that reproduces music, has changed the way we discover music: behind every song it suggests, lies a complex system that learns what we like, adapts to our moods and quietly shapes our sense of taste.
HOW SPOTIFY WORKS
The iconic app relies on powerful recommendation systems that combine several processes, in which we can find collaborative filtering, a complex type of recommender systems and audio analysis, which can be understood as a content based recommender.
Collaborative filtering compares your listening habits with those of millions of other users, identifying patterns and shared preferences.
Secondly, audio analysis breaks down each track into measurable components, analyzing, for example, tempo, energy, and frequencies allowing the app to ‘understand’ its DNA of the song.
In this way, thanks to these processes, Spotify is able to craft a detailed profile based on our listening behavior, in which each skip, replay or saved song becomes a powerful signal. Thanks to our actions, the app is then able to discover what we like and when we enjoy specific types of music, leading to accurate recommendations.
BREAKING DOWN THE MYTH: IS MUSIC REALLY PERSONAL?
The way Spotify works reveals that music taste can be influenced and studied according to objective parameters, breaking down the myth of it being personal and subjective.
Also, if we take into consideration the suggestions given by the app, Spotify has another powerful ability: it can shape our tastes, expanding them, as it happens with the ‘Discover Weekly’ playlist, and, sometimes, narrowing them, creating comfortable playlists in which one can deep dive. In this sense, Spotify plays an active, yet silent, role in guiding our music taste and identity, influencing the soundtrack of our lives.
Every playlist you can see on the app is the result of a complete and personalized calculation, which takes objective parameters into consideration, reshaping the way we think about music. In this way, your listening history becomes a form of digital self-portrait, highlighting recurring moods, favourite genres, and meaningful artists, primarily based on objective and measurable data.
DOES IT WORKS?
Spotify’s recommender system is widely considered one of the most effective in the world and its success lies in how accurately it captures the user behavior. The system works because it doesn’t use just a single process, but many: from the frequency analysis to the beat analysis, to the collaborative filtering.
However, its functioning is strongly linked to the user interaction: this means that for new users, the suggestion may be off track, and improves rapidly as data accumulates. This phenomenon can be observed within different apps, such as TikTok and Instagram, which use different, yet precise, recommender systems.
Overall, Spotify’s recommender system works remarkably well: it learns quickly, adapts continuously, and succeeds at delivering music that feels enjoyable for the users. By analysing many objective aspects of music and data captured thanks to the interaction within the app, Spotify is able to enrich how we listen, discover, and express ourselves.
Spotify’s algorithms do more than suggest songs: they collaborate with us, enriching and sometimes reshaping our tastes and the way we listen to music, showing the powerful, yet hidden, force of recommender systems and data.
CIMO 