Neidhardt, J. (2024). Transforming Recommender Systems: Balancing Personalization, Fairness, and Human Values. In K. Larson (Ed.), Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence (pp. 8559–8564). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2024/982
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
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ISBN:
978-1-956792-04-1
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Datum (veröffentlicht):
9-Aug-2024
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Veranstaltungsname:
Thirty-Third International Joint Conference on Artificial Intelligence (IJCAI)
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Veranstaltungszeitraum:
3-Aug-2024 - 9-Aug-2024
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Veranstaltungsort:
Jeju, Südkorea
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Umfang:
6
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Verlag:
International Joint Conferences on Artificial Intelligence
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Keywords:
Recommender systems; Personalization and user modeling; Fairness and diversity; Societal impact of AI
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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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Projekttitel:
Christian Doppler Labor für Weiterentwicklung des State-of-the-Art von Recommender-Systemen in mehreren Domänen: CDL Neidhardt (Christian Doppler Forschungsgesells)
