#8: Music Recommender Systems, Fairness and Evaluation with Christine Bauer

In episode number eight of Recsperts we discuss music recommender systems, the meaning of artist fairness and perspectives on recommender evaluation. I talk to Christine Bauer, who is an assistant professor at the University of Utrecht and co-organizer of the PERSPECTIVES workshop. Her research deals with context-aware recommender systems as well as the role of fairness in the music domain. Christine published work at many conferences like CHI, CHIIR, ICIS, and WWW.
In episode number eight of Recsperts we discuss music recommender systems, the meaning of artist fairness and perspectives on recommender evaluation. I talk to Christine Bauer, who is an assistant professor at the University of Utrecht and co-organizer of the PERSPECTIVES workshop. Her research deals with context-aware recommender systems as well as the role of fairness in the music domain. Christine published work at many conferences like CHI, CHIIR, ICIS, and WWW.

In this episode we talk about the specifics of recommenders in the music streaming domain. In particular, we discuss the interests of different stakeholders, like users, the platform, or artists. Christine Bauer presents insights from her research on fairness with respect to the representation of artists and their interests. We talk about gender imbalance and how recommender systems could serve as a tool to counteract existing imbalances instead of reinforcing them, for example with simulations and reranking. In addition, we talk about the lack of multi-method evaluation and how open datasets incline researchers to focus too much on offline evaluation. In contrast, Christine argues for more user studies and online evaluation.

We wrap up with some final remarks on context-aware recommender systems and the potential of sensor data for improving context-aware personalization.

Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.

Links from the Episode:

Papers:

General Links: