Title: The Music Streaming Sessions Dataset,
Abstract: 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.
These problems can often be reduced to a combination of 1) sequentially
recommending items to the user, and 2) exploiting the user's interactions with
the items as feedback for the machine learning model. 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 approximately 150 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.