<div class="csl-bib-body">
<div class="csl-entry">Basso, L., Nalis-Neuner, I., & Neidhardt, J. (2023). News Diversity and Well-Being – An Experimental Exploration Of Diversity-Aware Recommender Systems. In <i>FAccTRec Program</i>. 6th FAccTRec Workshop on Responsible Recommendation at RecSys 2023, Singapur, Singapore.</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/193212
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dc.description.abstract
The demand for socially responsible designs for news recommender systems is currently of the utmost relevance. This paper presents a novel and interdisciplinary approach, bringing together psychol- ogists and computer scientists, to examine the impact of diverse news recommendations on individual users. In this experimental study, participants were divided into two groups, interacting with either a diverse news recommender system (experimental group) or a non-diversified system (control group). Subjective well-being and personal evaluations of the recommender system were measured. Although the study did not find a significant positive impact on participants’ subjective well-being after consuming more diverse news, this preliminary investigation opens avenues for further re- search. This study sets the stage for future investigations, providing valuable insights and highlighting the complexities of promoting diverse news consumption through recommender systems. Further research is warranted to explore potential enhancements and refine the understanding of the relationship between diversified news recommendations and user well-being. This contribution lays a groundstone for further research on responsibilities and how to implement basic human values, which are important to sustain and advance the democratic society we live in.
en
dc.description.sponsorship
Christian Doppler Forschungsgesells
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dc.language.iso
en
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dc.subject
recommender systems
en
dc.subject
responsible recommendation
en
dc.subject
diversity
en
dc.subject
well- being
en
dc.subject
user study
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dc.title
News Diversity and Well-Being – An Experimental Exploration Of Diversity-Aware Recommender Systems
en
dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.relation.grantno
CDL Neidhardt
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dc.type.category
Full-Paper Contribution
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tuw.booktitle
FAccTRec Program
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tuw.peerreviewed
true
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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
C3
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tuw.researchTopic.name
Computational System Design
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tuw.researchTopic.value
100
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tuw.publication.orgunit
E194-04 - Forschungsbereich Data Science
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dc.description.numberOfPages
6
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tuw.author.orcid
0009-0009-8166-5488
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tuw.author.orcid
0000-0001-7184-1841
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tuw.event.name
6th FAccTRec Workshop on Responsible Recommendation at RecSys 2023
en
tuw.event.startdate
18-09-2023
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tuw.event.enddate
18-09-2023
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tuw.event.online
Hybrid
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tuw.event.type
Event for scientific audience
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tuw.event.place
Singapur
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tuw.event.country
SG
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tuw.event.presenter
Nalis-Neuner, Irina
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tuw.presentation.online
Online
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tuw.event.track
Multi Track
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wb.sciencebranch
Informatik
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wb.sciencebranch
Psychologie
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wb.sciencebranch.oefos
1020
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wb.sciencebranch.oefos
5010
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wb.sciencebranch.value
60
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wb.sciencebranch.value
40
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item.languageiso639-1
en
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item.openairetype
conference paper
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item.grantfulltext
restricted
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item.fulltext
no Fulltext
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item.cerifentitytype
Publications
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item.openairecristype
http://purl.org/coar/resource_type/c_5794
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crisitem.author.dept
E194-04 - Forschungsbereich Data Science
-
crisitem.author.dept
E194-04 - Forschungsbereich Data Science
-
crisitem.author.orcid
0009-0009-8166-5488
-
crisitem.author.orcid
0000-0001-7184-1841
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crisitem.author.parentorg
E194 - Institut für Information Systems Engineering
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crisitem.author.parentorg
E194 - Institut für Information Systems Engineering