Investigating the Impact of Audio States & Transitions for Track Sequencing in Music Streaming Sessions


Music streaming is inherently sequential in nature, with track sequence information playing a key role in user satisfaction with recommended music. In this work, we investigate the role audio characteristics of music content play in understanding music streaming sessions. Focusing on 18 audio attributes (e.g. dancability, acousticness, energy), we formulate audio transitioning in a session as a multiple changepoint detection problem, and extract latent states of different audio attributes within each session. Based on insights from large scale music streaming data from a popular music streaming platform, we investigate questions around the extent to which audio characteristics fluctuate within streaming sessions, the heterogeneity across different audio attributes and their impact on user satisfaction. Furthermore, we demonstrate the promise of such audio-based characterizing of sessions in better sequencing tracks in a session, and highlight the potential gains in user satisfaction on offer. We discuss implications on the design of track sequencing models, and identify important prediction tasks to further research on the topic.


April 2022 | The Web Conference (WWW)

Sequential Recommendation via Stochastic Self-Attention

Ziwei Fan, Zhiwei Liu, Alice Wang, Zahra Nazari, Lei Zheng, Hao Peng, Philip S. Yu

April 2022 | The Web Conference (WWW)

Using Survival Models to Estimate Long-Term Engagement in Online Experiments

Praveen Chandar, Brian St. Thomas, Lucas Maystre, Vijay Pappu, Roberto Sanchis-Ojeda, Tiffany Wu, Ben Carterette, Mounia Lalmas, Tony Jebara

April 2022 | The Web Conference (WWW)

Choice of Implicit Signal Matters: Accounting for User Aspirations in Podcast Recommendations

Zahra Nazari, Praveen Chandar, Ghazal Fazelnia, Catie Edrwards, Ben Carterette, Mounia Lalmas