Collaborative Filtering

Collaborative filtering relies on implicit user feedback - preferential data that is not explicitly supplied by the user.

Spotify creates a back end profile based on the user’s personal taste, which is crunched and used by the algorithm into the music landscape of millions of songs - to find music that the user may like but has not streamed yet. These recommendations keep strengthening as the user uses it more, to the point where no two Spotify users have the exact same recommendations.

Spotify’s algorithm is driven by statistics. The platform is constantly watching how its hundreds of millions of users engage with different types of music in order to feed them more of what they like.

The algorithm takes loads of stats into account when deciding which songs to suggest to its users.

Important data points that form implicit feedback for Spotify's algorithm include:

• Listening history (mood, style, genre)

• Skip rate (less skips = more recommendations)

• Listening time (getting past 30 seconds is key)

• Playlist features (inclusions across all personal, indie & official playlists)