Record link: 
http://hdl.handle.net/20.500.12708/199513
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Title: 
Evaluating Group Fairness in News Recommendations: A Comparative Study of Algorithms and Metrics
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Citation: 
Huebner, B., Kolb, T. E., & Neidhardt, J. (2024). Evaluating Group Fairness in News Recommendations: A Comparative Study of Algorithms and Metrics. In Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization (pp. 337–346). https://doi.org/10.1145/3631700.3664897
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Publisher DOI: 
10.1145/3631700.3664897
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Publication Type: 
Inproceedings - Full-Paper Contribution
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Language: 
English
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Authors: 
Huebner, Blake 
Kolb, Thomas Elmar 
Neidhardt, Julia 
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Organisational Unit: 
E194-04 - Forschungsbereich Data Science 
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Published in: 
Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization 
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ISBN: 
9798400704666
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DOI of the book: 
10.1145/3631700
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Date (published): 
28-Jun-2024
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Event name: 
3rd Workshop on Group Modeling, Adaptation and Personalization at ACM UMAP 2024 
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Event date: 
1-Jul-2024 - 4-Jul-2024
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Event place: 
Cagliari, Italy
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Number of Pages: 
10
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Keywords: 
Recommender Systems (RS); Fairness in Recommender Systems; Diversity in Recommender Systems; Beyond-accuracy Measures; FOCF; PFCN
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Project title: 
Christian Doppler Labor für Weiterentwicklung des State-of-the-Art von Recommender-Systemen in mehreren Domänen: CDL Neidhardt (Christian Doppler Forschungsgesells) 
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Research Areas: 
Information Systems Engineering: 100%
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Science Branch: 
1020 - Informatik: 100%
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Appears in Collections:
Conference Paper

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