61 citations · 234 across the 13 of their papers we have counts for
9 papers · 1 filter
It's Not You, It's Me: The Impact of Choice Models and Ranking Strategies on Gender Imbalance in Music Recommendation
Andres Ferraro, Michael D. Ekstrand, Christine Bauer
As recommender systems are prone to various biases, mitigation approaches are needed to ensure that recommendations are fair to various stakeholders. One particular concern in musi…
Report from Dagstuhl Seminar 23031: Frontiers of Information Access Experimentation for Research and Education
Christine Bauer, Ben Carterette, Nicola Ferro +1
This report documents the program and the outcomes of Dagstuhl Seminar 23031 ``Frontiers of Information Access Experimentation for Research and Education'', which brought together…
A Stakeholder-Centered View on Fairness in Music Recommender Systems
Karlijn Dinnissen, Christine Bauer
Our narrative literature review acknowledges that, although there is an increasing interest in recommender system fairness in general, the music domain has received relatively litt…
Support the Underground: Characteristics of Beyond-Mainstream Music Listeners
Dominik Kowald, Peter Muellner, Eva Zangerle +3
Music recommender systems have become an integral part of music streaming services such as Spotify and Last.fm to assist users navigating the extensive music collections offered by…
Listener Modeling and Context-aware Music Recommendation Based on Country Archetypes
Markus Schedl, Christine Bauer, Wolfgang Reisinger +2
Music preferences are strongly shaped by the cultural and socio-economic background of the listener, which is reflected, to a considerable extent, in country-specific music listeni…
Online Music Listening Culture of Kids and Adolescents: Listening Analysis and Music Recommendation Tailored to the Young
Markus Schedl, Christine Bauer
In this paper, we analyze a large dataset of user-generated music listening events from Last.fm, focusing on users aged 6 to 18 years. Our contribution is two-fold. First, we study…
Leveraging Multi-Method Evaluation for Multi-Stakeholder Settings
Christine Bauer, Eva Zangerle
In this paper, we focus on recommendation settings with multiple stakeholders with possibly varying goals and interests, and argue that a single evaluation method or measure is not…
Global and country-specific mainstreaminess measures: Definitions, analysis, and usage for improving personalized music recommendation systems
Christine Bauer, Markus Schedl
Popularity-based approaches are widely adopted in music recommendation systems, both in industry and research. However, as the popularity distribution of music items typically is a…
Allowing for equal opportunities for artists in music recommendation
Christine Bauer
Promoting diversity in the music sector is widely discussed on the media. While the major problem may lie deep in our society, music information retrieval contributes to promoting…