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Spotify Music Recommendations through Algorithms

Article Link: How Spotify’s Algorithm Knows Exactly What You Want to Listen To

Spotify uses a powerful set of algorithms based on a user’s music listening history and personal details to shape their experience and get users to listen to more music. These algorithms have influenced a user’s home screen, custom-curated playlists such as “Discover Weekly” and “Daily Mix”. Spotify also has an automatic playlist continuation feature that analyzes the song in a certain playlist and predicts similar music that match the playlist after all the songs in the playlist have been played. By tracking and logging each user’s actions on the platform, Spotify is able to refine the success of its algorithms. Spotify has also looked into other data such as users’ self-reported gender and age and location, which factor into musical taste according to studies of overall trends.

Due to music data containing few words (besides song title, genre, artist), Spotify needs to develop complex algorithms for song suggestions. Similar to how PageRank uses clicks as votes, Spotify’s algorithm analyzes how long a user streams a song for. There are “thumbs down” and “thumbs up” buttons for users to use to help adjust recommendations accordingly. A user skipping a song before 30 seconds would also be the equivalent of down-voting a suggested song. Based on user feedback, Spotify updates the node’s endorsement and refines the quality of music suggestions. Spotify also analyzes past music history, songs played more frequently/repeatedly and songs a user has added to personal playlists to break down the music taste. The algorithm updates constantly to take account a user’s shifting music taste, similar to how PageRank updates .


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October 2019