activity
20192024
most citedGlobal and country-specific mainstreaminess measures: Definitions, analysis, and usage for improving personalized music recommendation systems

61 citations · 234 across the 13 of their papers we have counts for

collaborators

13 papers

cs.IR2024★ 13 cited

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…

cs.IR2023★ 24 cited

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…

cs.IR2022★ 58 cited

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…

cs.HC2021★ 3 cited

What is fair? Exploring the artists' perspective on the fairness of music streaming platforms

Andres Ferraro, Xavier Serra, Christine Bauer

Music streaming platforms are currently among the main sources of music consumption, and the embedded recommender systems significantly influence what the users consume. There is a…

cs.IR2021★ 45 cited

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…

cs.IR2020★ 14 cited

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…

cs.CY2020★ 5 cited

An Open Model for Researching the Role of Culture in Online Self-Disclosure

Christine Bauer, Katharina Sophie Schmid, Christine Strauss

The analysis of consumers' personal information (PI) is a significant source to learn about consumers. In online settings, many consumers disclose PI abundantly -- this is particul…

cs.IR2019★ 2 cited

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…

cs.IR2019★ 6 cited

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…

cs.IR2019★ 61 cited

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…

cs.OH2019

Context Adaptivity as Enabler for Meaningful Pervasive Advertising

Christine Bauer

Socio-demographic user profiles are currently regarded as the most convenient base for successful personalized advertising. However, signs point to the dormant power of context rec…

cs.HC2019★ 1 cited

The Potential of the Confluence of Theoretical and Algorithmic Modeling in Music Recommendation

Christine Bauer

The task of a music recommender system is to predict what music item a particular user would like to listen to next. This position paper discusses the main challenges of the music…

cs.IR2019★ 2 cited

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…