Transforming Recommender Systems: Balancing Personalization, Fairness, and Human Values

Transforming Recommender Systems: Balancing Personalization, Fairness, and Human Values

Julia Neidhardt

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Early Career. Pages 8559-8564. https://doi.org/10.24963/ijcai.2024/982

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.
Keywords:
Data Mining: DM: Recommender systems
Humans and AI: HAI: Personalization and user modeling
AI Ethics, Trust, Fairness: ETF: Fairness and diversity
AI Ethics, Trust, Fairness: ETF: Societal impact of AI