The Music Streaming Sessions Dataset
This dataset addresses a gap for researchers in music streaming and recommendation systems, though it is incremental as it primarily provides new data rather than novel methods.
The authors tackled the lack of public datasets for modeling user interactions in online streaming services by releasing the Music Streaming Sessions Dataset (MSSD), which includes 160 million listening sessions and metadata for 3.7 million tracks, enabling research on user behavior and recommendations.
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.