Towards Trustworthy Multi-stakeholder Recommender Systems

Katarína Marcinc̆inová, Adrian Gavornik, Matús̆ Mesarc̆ík, Michal Kompan
Proceedings of Fourth European Workshop on Algorithmic Fairness, PMLR 294:446-451, 2025.

Abstract

Recommender system (RS) nowadays has to reflect, combine, and solve often contrasting requirements and expectations from dozens of stakeholders. In fact, the problem is even more complicated as each of the stakeholders can simultaneously have various objectives creating a diverse and complicated environment with several stakeholders and their objectives in which a RS operates. To address this problems the Multi-objective and Multi-stakeholder RS have been studied in the literature. However, a limited attention has been paid to trustworthiness aspects of such RS, which slowly becomes a new standard. In this paper, we highlight open challenges and important questions which need to be addressed on the way to trustworthy Multi-stakeholder RS.

Cite this Paper

BibTeX
@InProceedings{pmlr-v294-marcinc-inova25a, title = {Towards Trustworthy Multi-stakeholder Recommender Systems}, author = {Marcin\u{c}inov\'a, Katar\'ina and Gavornik, Adrian and Mesar\u{c}\'ik, Mat\'u\u{s} and Kompan, Michal}, booktitle = {Proceedings of Fourth European Workshop on Algorithmic Fairness}, pages = {446--451}, year = {2025}, editor = {Weerts, Hilde and Pechenizkiy, Mykola and Allhutter, Doris and Corrêa, Ana Maria and Grote, Thomas and Liem, Cynthia}, volume = {294}, series = {Proceedings of Machine Learning Research}, month = {30 Jun--02 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v294/main/assets/marcinc-inova25a/marcinc-inova25a.pdf}, url = {https://proceedings.mlr.press/v294/marcinc-inova25a.html}, abstract = {Recommender system (RS) nowadays has to reflect, combine, and solve often contrasting requirements and expectations from dozens of stakeholders. In fact, the problem is even more complicated as each of the stakeholders can simultaneously have various objectives creating a diverse and complicated environment with several stakeholders and their objectives in which a RS operates. To address this problems the Multi-objective and Multi-stakeholder RS have been studied in the literature. However, a limited attention has been paid to trustworthiness aspects of such RS, which slowly becomes a new standard. In this paper, we highlight open challenges and important questions which need to be addressed on the way to trustworthy Multi-stakeholder RS.} }
Endnote
%0 Conference Paper %T Towards Trustworthy Multi-stakeholder Recommender Systems %A Katarína Marcinc̆inová %A Adrian Gavornik %A Matús̆ Mesarc̆ík %A Michal Kompan %B Proceedings of Fourth European Workshop on Algorithmic Fairness %C Proceedings of Machine Learning Research %D 2025 %E Hilde Weerts %E Mykola Pechenizkiy %E Doris Allhutter %E Ana Maria Corrêa %E Thomas Grote %E Cynthia Liem %F pmlr-v294-marcinc-inova25a %I PMLR %P 446--451 %U https://proceedings.mlr.press/v294/marcinc-inova25a.html %V 294 %X Recommender system (RS) nowadays has to reflect, combine, and solve often contrasting requirements and expectations from dozens of stakeholders. In fact, the problem is even more complicated as each of the stakeholders can simultaneously have various objectives creating a diverse and complicated environment with several stakeholders and their objectives in which a RS operates. To address this problems the Multi-objective and Multi-stakeholder RS have been studied in the literature. However, a limited attention has been paid to trustworthiness aspects of such RS, which slowly becomes a new standard. In this paper, we highlight open challenges and important questions which need to be addressed on the way to trustworthy Multi-stakeholder RS.
APA
Marcinc̆inová, K., Gavornik, A., Mesarc̆ík, M. & Kompan, M.. (2025). Towards Trustworthy Multi-stakeholder Recommender Systems. Proceedings of Fourth European Workshop on Algorithmic Fairness, in Proceedings of Machine Learning Research 294:446-451 Available from https://proceedings.mlr.press/v294/marcinc-inova25a.html.

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