# The Music Streaming Sessions Dataset

arXiv: https://arxiv.org/abs/1901.09851 · HTML: https://mlsanity.com/paper/1901.09851/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2018.

## Built on by (most cited first, 18 of 18)

- [End-to-end Learnable Clustering for Intent Learning in Recommendation](https://mlsanity.com/paper/2401.05975/) (2024)
- [Automatic Music Playlist Generation via Simulation-based Reinforcement Learning](https://mlsanity.com/paper/2310.09123/) (2023)
- [CSRec: Rethinking Sequential Recommendation from A Causal Perspective](https://mlsanity.com/paper/2409.05872/) (2024)
- [Self Contrastive Learning for Session-based Recommendation](https://mlsanity.com/paper/2306.01266/) (2023)
- [You Don't Bring Me Flowers: Mitigating Unwanted Recommendations Through Conformal Risk Control](https://mlsanity.com/paper/2507.16829/) (2025)
- [Multi-Armed Bandit Problem with Temporally-Partitioned Rewards: When Partial Feedback Counts](https://mlsanity.com/paper/2206.00586/) (2022)
- [Structured Learning of Compositional Sequential Interventions](https://mlsanity.com/paper/2406.05745/) (2024)
- [CPMR: Context-Aware Incremental Sequential Recommendation with Pseudo-Multi-Task Learning](https://mlsanity.com/paper/2309.04802/) (2023)
- [Leveraging Negative Signals with Self-Attention for Sequential Music Recommendation](https://mlsanity.com/paper/2309.11623/) (2023)
- [Counterfactual Strategies for Markov Decision Processes](https://mlsanity.com/paper/2505.09412/) (2025)
- [MeloTune: On-Device Arousal Learning and Peer-to-Peer Mood Coupling for Proactive Music Curation](https://mlsanity.com/paper/2604.10815/) (2026)
- [Sequential Modelling with Applications to Music Recommendation, Fact-Checking, and Speed Reading](https://mlsanity.com/paper/2109.06736/) (2021)
- [Memory-Efficient Sequential Pattern Mining with Hybrid Tries](https://mlsanity.com/paper/2202.06834/) (2022)
- [Multi-Armed Bandits with Generalized Temporally-Partitioned Rewards](https://mlsanity.com/paper/2303.00620/) (2023)
- [Negative Feedback for Music Personalization](https://mlsanity.com/paper/2406.04488/) (2024)
- [Sequential choice in ordered bundles](https://mlsanity.com/paper/2410.21670/) (2024)
- [Semantic IDs for Music Recommendation](https://mlsanity.com/paper/2507.18800/) (2025)
- [Attribution-based Explanations for Markov Decision Processes](https://mlsanity.com/paper/2605.09780/) (2026)

## Abstract (arXiv)

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.