Privacy-Aware Contextual Bandit Models for Personalized Recommendations

Abstract

Personalized recommendation systems have become foundational to modern digital platforms, shaping information access, consumer behavior, and socio-technical interaction at scale. Contextual bandit models provide a principled framework for sequential decision-making under uncertainty, enabling adaptive personalization through continuous learning from user interactions. However, the increasing integration of such models into high-stakes environments raises significant privacy concerns, particularly as contextual features often encode sensitive behavioral, demographic, or situational information. This paper examines privacy-aware contextual bandit models through a system-level and infrastructural lens, emphasizing architectural trade-offs, governance mechanisms, and deployment constraints in large-scale recommendation environments. We explore how privacy-preserving mechanisms such as data minimization, perturbation-based learning, federated learning architectures, and secure aggregation influence model performance, robustness, and fairness. Rather than focusing on algorithmic derivations, we investigate the socio-technical embedding of these systems, including regulatory alignment, organizational accountability, and infrastructure sustainability. We further analyze how privacy constraints reshape exploration-exploitation dynamics, long-term utility optimization, and user trust calibration. Through cross-domain comparisons spanning e-commerce, digital media, healthcare recommendation systems, and smart city services, we highlight the systemic tensions between personalization fidelity and privacy preservation. The paper concludes by outlining emerging directions in privacy-aware adaptive systems, emphasizing hybrid architectures that integrate decentralized learning, policy-aware decision layers, and interpretable governance frameworks to support responsible AI deployment at scale.