The Music Streaming Sessions Dataset (short paper)


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.


August 2020 | KDD

Bandit based Optimization of Multiple Objectives on a Music Streaming Platform

Rishabh Mehrotra, Niannan Xue, Mounia Lalmas

August 2020 | KDD

Advances in Recommender Systems: From Multi-stakeholder Marketplaces to Automated RecSys

Rishabh Mehrotra, Ben Carterette, Yong Li, Quanming Yao, James Tin-Yau Kwok, Isabelle Guyon, Qiang Yang

August 2020 | KDD

Counterfactual Evaluation of Slate Recommendations with Sequential Reward Interactions

Praveen Chandar, James McInerney, Brian Brost, Rishabh Mehrotra, Benjamin Carterette