![]() ![]() To make things easy, I used the spotipy library in Python, which supports all of the features of the Spotify Web API. To access the Spotify API, you’ll need a Spotify account (free or premium), and a registered application. In this post, I’ll leverage Spotify’s “similar artists” API to build interactive network charts, visualizing how artists are linked together, as measured by the similarity of their fans. Obviously, this is a huge win for music data nerds everywhere. After authenticating and supply an artist id, the API will return a list of 20 similar artists. The cool thing is that Spotify exposes this discovery algorithm via API. It’s the data behind radio, genres, and Discover pages.” Glenn McDonald, Spotify’s data alchemist ( source) “Artist similarity is probably the second-most important piece of data we extract from listening patterns-after popularity. This metric is based on shared fans, meaning the more fans two artists have in common, the higher their similarity score. Listed on each artist page, the “Fans Also Like” section is an algorithmically populated discovery feature built using a metric called “ artist similarity”. FANS ALSO LIKE – A Spotify music discovery feature Lately, I’ve found new favorites through a Spotify feature called “Fans Also Like”. I usually rely on a mix of sources: websites like Pitchfork or Genius, subreddits like popheads or hiphodheads, and curated playlists like Get Turnt or Hot Rhythmic. In my experience, there’s no single discovery mechanism that delivers consistently. ![]()
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