<div class="csl-bib-body">
<div class="csl-entry">Neidhardt, J. (2024). Transforming Recommender Systems: Balancing Personalization, Fairness, and Human Values. In K. Larson (Ed.), <i>Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence</i> (pp. 8559–8564). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2024/982</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/210636
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dc.description.abstract
Recent advancements in recommender systems highlight the importance of metrics beyond accuracy, including diversity, serendipity, and fairness. This paper discusses various aspects of modern recommender systems, focusing on challenges such as preference elicitation, the complexity of human decision-making, and multi-domain applicability. The integration of Generative AI and Large Language Models offers enhanced personalization capabilities but also raises concerns regarding transparency and fairness. This work examines ongoing research efforts aimed at developing transparent, fair, and contextually aware systems. Our approach seeks to prioritize user wellbeing and responsibility, contributing to a more equitable and functional digital environment through advanced technologies and interdisciplinary insights.
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dc.description.sponsorship
Christian Doppler Forschungsgesells
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dc.language.iso
en
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dc.subject
Recommender systems
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dc.subject
Personalization and user modeling
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dc.subject
Fairness and diversity
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dc.subject
Societal impact of AI
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dc.title
Transforming Recommender Systems: Balancing Personalization, Fairness, and Human Values