Datasets › MSSD

MSSD (Music Streaming Sessions Dataset)

Introduced by Brian Brost et al. in The Music Streaming Sessions Dataset31 Dec 2018 archive 2025-07-28

The Spotify Music Streaming Sessions Dataset (MSSD) consists of 160 million streaming sessions with associated user interactions, audio features and metadata describing the tracks streamed during the sessions, and snapshots of the playlists listened to during the sessions.

This dataset enables research on important problems including how to model user listening and interaction behaviour in streaming, as well as Music Information Retrieval (MIR), and session-based sequential recommendations.

Benchmarks archive 2025-07-28

All 1 leaderboard whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.

First row (archive order)PaperCode
Sequential skip prediction MSSD Teacher mean average accuracy 84.9 Sequential Skip Prediction with Few-shot in Streamed... mimbres/SeqSkip 2 Compare

Papers archive 2025-07-28

1 shown of 1 paper with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 7. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.

DateSamples run Syntology
Sequential Skip Prediction with Few-shot in Streamed Music Contents 1 2 24 Jan 2019 not harvested

Dataset loaders archive 2025-07-28

1 loader as listed in the archive; links are outbound and not re-checked here.

Tasks archive 2025-07-28

License archive 2025-07-28

No licence recorded in the archive. Absence here is not a statement about the dataset's terms.

Modalities archive 2025-07-28

Languages archive 2025-07-28

No language tagged.

Variants archive 2025-07-28

  • MSSD

1 variant name, as the archive lists them.

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