Understanding and Evaluating User Satisfaction with Music Discovery


We study the use and evaluation of a system for supporting music discovery, the experience of finding and listening to content previously unknown to the user. We adopt a mixed methods approach, including interviews, unsupervised learning, survey research, and statistical modeling, to understand and evaluate user satisfaction in the context of discovery. User interviews and survey data show that users’ behaviors change according to their goals, such as listening to recommended tracks in the moment, or using recommendations as a starting point for exploration. We use these findings to develop a statistical model of user satisfaction at scale from interactions with a music streaming platform. We show that capturing users’ goals, their deviations from their usual behavior, and their peak interactions on individual tracks are informative for estimating user satisfaction. Finally, we present and validate heuristic metrics that are grounded in user experience for online evaluation of recommendation performance. Our findings, supported with evidence from both qualitative and quantitative studies, reveal new insights about user expectations with discovery and their behavioral responses to satisfying and dissatisfying systems.


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