Challenges in Translating Research to Practice for Evaluating Fairness & Bias in Recommendation Systems
Lex Beattie, Dan Taber, Henriette Cramer
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.
Lex Beattie, Dan Taber, Henriette Cramer
Maryam Aziz, Jesse Anderton, Kevin Jamieson, Alice Wang, Hugues Bouchard, Javed Aslam
M Iftekhar Tanveer, Diego Casabuena, Jussi Karlgren, Rosie Jones