The MSSD is a large-scale collection of user interaction data from a music streaming service, designed to support research in user behavior modeling, music information retrieval, and session-based recommendation systems. Released in 2019, this dataset contains approximately 160 million listening sessions, making it one of the most extensive datasets available for analyzing how users engage with music streaming platforms. It provides valuable insights into listening habits, session structures, and sequential user interactions, enabling researchers to study music recommendation, user retention, and engagement patterns. The dataset has a size of 70 GB and captures approximately 3.7 million unique tracks, covering a diverse range of musical content. Each session includes detailed user interactions, such as play, pause, skip, and seek actions, offering a granular view of how listeners interact with music over time. Additionally, it contains rich metadata and audio features for each track, including details such as track ID, artist name, album name, and genre, along with audio attributes like tempo, key, and loudness. These elements make the dataset highly valuable for both behavioral studies and technical research in music information retrieval.
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