=Paper=
{{Paper
|id=Vol-3657/keynote1
|storemode=property
|title=Trustworthy Recommender Systems
|pdfUrl=https://ceur-ws.org/Vol-3657/keynote.pdf
|volume=Vol-3657
|authors=Elisabeth Lex
|dblpUrl=https://dblp.org/rec/conf/hci-si/Lex23
}}
==Trustworthy Recommender Systems==
Trustworthy Recommender Systems
Elisabeth Lex1
1
Graz University of Technology, Rechbauerstraße 12, 8010, Graz, Austria
Abstract
Recommender systems play a pivotal role in shaping our digital experiences, influencing the content
we see online, the products we consider purchasing, and the entertainment choices we make, such as
which movies to watch. The increased adoption of deep learning technologies in recommender systems,
while enhancing their effectiveness, has also raised substantial concerns regarding their transparency
and trustworthiness. Critical issues such as bias, fairness, and privacy are increasingly coming under
scrutiny, both in public discourse and academic research. In response, there’s a growing momentum in
developing recommender systems that are not only efficient but also uphold these ethical standards.
HCI SI 2023: Human-Computer Interaction Slovenia 2023, January 26, 2024, Maribor, Slovenia
$ elisabeth.lex@tugraz.at (E. Lex)
https://elisabethlex.info/ (E. Lex)
0000-0001-5293-2967 (E. Lex)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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