<div class="csl-bib-body">
<div class="csl-entry">Kolb, T. E., Nalis, I., & Neidhardt, J. (2025). Bridging Preferences: Multi-Stakeholder Insights on Ideal News Recommendations. In <i>UMAP ’25: Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization</i> (pp. 268–272). Association for Computing Machinery. https://doi.org/10.1145/3699682.3728355</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/224466
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dc.description.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.
en
dc.description.sponsorship
Christian Doppler Forschungsgesells
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dc.language.iso
en
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dc.subject
beyond accuracy measures
en
dc.subject
multistakeholder
en
dc.subject
news recommendation
en
dc.subject
responsible recommendation
en
dc.subject
user study
en
dc.title
Bridging Preferences: Multi-Stakeholder Insights on Ideal News Recommendations
en
dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.relation.isbn
979-8-4007-1313-2
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dc.relation.doi
10.1145/3699682
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dc.description.startpage
268
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dc.description.endpage
272
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dc.relation.grantno
CDL Neidhardt
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dc.type.category
Full-Paper Contribution
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tuw.booktitle
UMAP '25: Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization
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tuw.peerreviewed
true
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tuw.relation.publisher
Association for Computing Machinery
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tuw.relation.publisherplace
New York
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tuw.project.title
Christian Doppler Labor für Weiterentwicklung des State-of-the-Art von Recommender-Systemen in mehreren Domänen
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tuw.researchTopic.id
I4
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tuw.researchTopic.name
Information Systems Engineering
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tuw.researchTopic.value
100
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tuw.publication.orgunit
E194-04 - Forschungsbereich Data Science
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tuw.publication.orgunit
E056-23 - Fachbereich Innovative Combinations and Applications of AI and ML (iCAIML)
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tuw.publisher.doi
10.1145/3699682.3728355
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dc.description.numberOfPages
5
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tuw.author.orcid
0000-0002-2340-0854
-
tuw.author.orcid
0000-0001-7184-1841
-
tuw.event.name
UMAP '25: 33rd ACM Conference on User Modeling, Adaptation and Personalization
en
tuw.event.startdate
16-06-2025
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tuw.event.enddate
19-06-2025
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tuw.event.online
On Site
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tuw.event.type
Event for scientific audience
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tuw.event.place
New York City
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tuw.event.country
US
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tuw.event.presenter
Kolb, Thomas Elmar
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wb.sciencebranch
Informatik
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wb.sciencebranch.oefos
1020
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wb.sciencebranch.value
100
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item.openairetype
conference paper
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item.openairecristype
http://purl.org/coar/resource_type/c_5794
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item.cerifentitytype
Publications
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item.languageiso639-1
en
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item.grantfulltext
none
-
item.fulltext
no Fulltext
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crisitem.author.dept
E194-04 - Forschungsbereich Data Science
-
crisitem.author.dept
E194-04 - Forschungsbereich Data Science
-
crisitem.author.dept
E194-04 - Forschungsbereich Data Science
-
crisitem.author.orcid
0000-0002-2340-0854
-
crisitem.author.orcid
0000-0001-7184-1841
-
crisitem.author.parentorg
E194 - Institut für Information Systems Engineering
-
crisitem.author.parentorg
E194 - Institut für Information Systems Engineering
-
crisitem.author.parentorg
E194 - Institut für Information Systems Engineering
