Representation, Exploration, and Recommendation of Playlists: Dataset

This dataset was created using Spotify developer API. It consists of user-created as well as Spotify-curated playlists.
The dataset consists of 1 million playlists, 3 million unique tracks, 3 million unique albums, and 1.3 million artists.
The data is stored in a SQL database, with the primary entities being songs, albums, artists, and playlists.
Each of the aforementioned entities are represented by unique IDs (Spotify URI).
Data is stored into following tables:

  1. Album
  2. Artist
  3. Track
  4. Playlist
Album

| Id | Name | Uri |

Spotify Album Documentation

Id: Album ID as provided by Spotify
Name: Album Name as provided by Spotify
URI: Album URI as provided by Spotify


Artist

| Id | Name | Uri |


Spotify Artist Documentation

Id: Artist ID as provided by Spotify
Name: Artist Name as provided by Spotify
URI: Artist URI as provided by Spotify


Track

| Id | Name | Duration | Popularity | Explicit | Preview_Url | Uri | Album_id |


Spotify Track Documentation

Id: Track ID as provided by Spotify
Name: Track Name as provided by Spotify
Duration: Track Duration (in milliseconds) as provided by Spotify
Popularity: Track Popularity as provided by Spotify
Explicit: Whether the track has explicit lyrics or not. (true or false)
Preview_Url: A link to a 30 second preview (MP3 format) of the track. Can be null
Uri: Track Uri as provided by Spotify
Album_id: Album Id to which the track belongs


Playlist

| Id | Name | Followers | Uri | Total_tracks |


Spotify Playlist Documentation

Id: Playlist ID as provided by Spotify
Name: Playlist Name as provided by Spotify
Followers: Playlist Followers as provided by Spotify
Uri: Playlist Uri as provided by Spotify
Total_tracks: Total number of tracks in the playlist.


To access the dataset, please send an email request to ppapreja [at] asu [dot] edu.

If you use this dataset, please cite:


Papreja P., Venkateswara H., Panchanathan S. (2020) Representation, Exploration and Recommendation of Playlists. In: Cellier P., Driessens K. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019. Communications in Computer and Information Science, vol 1168. Springer, Cham