AI-Based Digital Twin Modeling of Human Preferences and Self-Concept for Personalized Recommendation Systems
Keywords:
Digital Twin, User Modeling, Self-Concept, Recommendation Systems, Personalization, Artificial Intelligence, Ethical AI, System ArchitectureAbstract
Personalized recommendation systems have evolved from collaborative filtering engines into complex socio-technical infrastructures that mediate digital experience. A frontier emerging in this evolution is the use of AI-based digital twin modeling to capture not only explicit preferences but also the implicit and evolving self-concept of the individual. This paper develops a system-level analysis of such digital twins, focusing on architectural paradigms, data governance, computational trade-offs, and long-term sustainability. We argue that modeling human self-concept as a dynamic, multi-layered digital twin introduces distinct challenges for latency-sensitive recommendation delivery, privacy preservation, fairness, and infrastructural resilience. Drawing together perspectives from user modeling, cognitive psychology, distributed systems, and AI ethics, the paper examines how self-concept representations can be instantiated, synchronized with behavioral data streams, and protected against adversarial manipulation. We highlight the governance structures required to prevent exploitative personalization and the policy frameworks that must accompany large-scale deployment. Throughout, we emphasize the tension between rich inner-state modeling and the need for transparent, auditable, and ethically bounded recommender ecosystems. The analysis concludes by outlining forward-looking research directions that integrate digital twin sustainability, cross-domain interoperability, and participatory design into the next generation of human-centered recommendation infrastructure.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
