Record link: 
http://hdl.handle.net/20.500.12708/224466
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Title: 
Bridging Preferences: Multi-Stakeholder Insights on Ideal News Recommendations
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Citation: 
Kolb, T. E., Nalis, I., & Neidhardt, J. (2025). Bridging Preferences: Multi-Stakeholder Insights on Ideal News Recommendations. In UMAP ’25: Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization (pp. 268–272). Association for Computing Machinery. https://doi.org/10.1145/3699682.3728355
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Publisher DOI: 
10.1145/3699682.3728355
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Publication Type: 
Inproceedings - Full-Paper Contribution
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Language: 
English
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Authors: 
Kolb, Thomas Elmar 
Nalis, Irina 
Neidhardt, Julia 
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Organisational Unit: 
E194-04 - Forschungsbereich Data Science 
E056-23 - Fachbereich Innovative Combinations and Applications of AI and ML (iCAIML) 
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Published in: 
UMAP '25: Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization 
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ISBN: 
979-8-4007-1313-2
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DOI of the book: 
10.1145/3699682
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Date (published): 
13-Jun-2025
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Event name: 
UMAP '25: 33rd ACM Conference on User Modeling, Adaptation and Personalization 
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Event date: 
16-Jun-2025 - 19-Jun-2025
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Event place: 
New York City, United States of America (the)
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Number of Pages: 
5
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Publisher: 
Association for Computing Machinery, New York
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Peer reviewed: 
Yes
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Keywords: 
beyond accuracy measures; multistakeholder; news recommendation; responsible recommendation; user study
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Abstract: 
In the evolving realm of recommender systems, our study contributes to the understanding of potential improvements in news recommendation beyond accuracy. Central to our research is the integration of insights from news industry experts and prospective readers, compared with automated news recommendations. We conducted a labeling study with 168 articles, using Best-Worst Scaling (BWS) for ranking and topic modeling. This approach enabled a thorough examination of stakeholder expectations for ideal reading recommendations, specifically by investigating the gap between stated and revealed preferences. Our findings show alignment in ranking behavior among journalists, prospective readers, and the BM-25 algorithm. However, preferences for different beyond-accuracy measures varied. Accompanying this work, a corpus of news articles and the labeled rankings have been made available.
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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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