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Found 9 papers, 1 papers with code
Report from Dagstuhl Seminar 23031: Frontiers of Information Access Experimentation for Research and Education
This report documents the program and the outcomes of Dagstuhl Seminar 23031 ``Frontiers of Information Access Experimentation for Research and Education'', which brought together 37 participants from 12 countries.
A Stakeholder-Centered View on Fairness in Music Recommender Systems
How can we move forward to a focus on improving fairness aspects in these recommender systems?
Support the Underground: Characteristics of Beyond-Mainstream Music Listeners
In this paper, we study the characteristics of beyond-mainstream music and music listeners and analyze to what extent these characteristics impact the quality of music recommendations provided.
Listener Modeling and Context-aware Music Recommendation Based on Country Archetypes
To complement and extend these results, the article at hand delivers the following major contributions: First, using state-of-the-art unsupervised learning techniques, we identify and thoroughly investigate (1) country profiles of music preferences on the fine-grained level of music tracks (in contrast to earlier work that relied on music preferences on the artist level) and (2) country archetypes that subsume countries sharing similar patterns of listening preferences.
Online Music Listening Culture of Kids and Adolescents: Listening Analysis and Music Recommendation Tailored to the Young
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.
Collaborative Filtering
Cultural Vocal Bursts Intensity Prediction
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Global and country-specific mainstreaminess measures: Definitions, analysis, and usage for improving personalized music recommendation systems
We conduct rating prediction experiments in which we tailor recommendations to a user's level of preference for the music mainstream using the proposed 6 mainstreaminess measures.
Leveraging Multi-Method Evaluation for Multi-Stakeholder Settings
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 able to evaluate all relevant aspects in such a complex setting.
The Potential of the Confluence of Theoretical and Algorithmic Modeling in Music Recommendation
The task of a music recommender system is to predict what music item a particular user would like to listen to next.
Allowing for equal opportunities for artists in music recommendation
Promoting diversity in the music sector is widely discussed on the media.
