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.


October 2020 | CIKM

Query Understanding for Surfacing Under-served Music Content

Federico Tomasi, Rishabh Mehrotra, Aasish Pappu, Judith Bütepage, Brian Brost, Hugo Galvão, Mounia Lalmas

September 2020 | RecSys

Inferring the Causal Impact of New Track Releases on Music Recommendation Platforms through Counterfactual Predictions

Rishabh Mehrotra, Prasanta Bhattacharya, Mounia Lalmas

September 2020 | RecSys

Investigating Listeners’ Responses to Divergent Recommendations

Rishabh Mehrotra, Chirag Shah, Benjamin Carterette