![]() Why do certain songs go together? What is the difference between “ Beach Vibes” and “ Forest Vibes”? And what words do people use to describe which playlists?īy learning more about nature of playlists, we may also be able to suggest other tracks that a listener would enjoy in the context of a given playlist. By learning from the playlists that people create, we can learn all sorts of things about the deep relationship between people and music. The other thing we love here at Spotify is playlist research. ![]() People create playlists for all sorts of reasons: some playlists group together music categorically (e.g., by genre, artist, year, or city), by mood, theme, or occasion (e.g., romantic, sad, holiday), or for a particular purpose (e.g., focus, workout). Some playlists are even made to land a dream job, or to send a message to someone special. To date, over 4 billion playlists have been created and shared by Spotify users. In fact, the Digital Music Alliance, in their 2018 Annual Music Report, state that 54% of consumers say that playlists are replacing albums in their listening habits.īut our users don’t love just listening to playlists, they also love creating them. Playlists like Today’s Top Hits and RapCaviar have millions of loyal followers, while Discover Weekly and Daily Mix are just a couple of our personalized playlists made especially to match your unique musical tastes. This is an open-ended challenge intended to encourage research in music recommendations, and no prizes will be awarded (other than bragging rights). The evaluation task is automatic playlist continuation: given a seed playlist title and/or initial set of tracks in a playlist, to predict the subsequent tracks in that playlist. The dataset contains 1,000,000 playlists, including playlist titles and track titles, created by users on the Spotify platform between January 2010 and October 2017. ![]() It is a continuation of the RecSys Challenge 2018, which ran from January to July 2018. The Spotify Million Playlist Dataset Challenge consists of a dataset and evaluation to enable research in music recommendations. ![]()
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