  At the core of many important machine learning problems faced by online
streaming services is a need to model how users interact with the content they
are served. Unfortunately, there are no public datasets currently available
that enable researchers to explore this topic. In order to spur that research,
we release the Music Streaming Sessions Dataset (MSSD), which consists of 160
million listening sessions and associated user actions. Furthermore, we provide
audio features and metadata for the approximately 3.7 million unique tracks
referred to in the logs. This is the largest collection of such track metadata
currently available to the public. 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. Additionally, a subset of sessions were collected
using a uniformly random recommendation setting, enabling their use for
counterfactual evaluation of such sequential recommendations. Finally, we
provide an analysis of user behavior and suggest further research problems
which can be addressed using the dataset.
