Explainable AI for Personalized Recommendation and Context-Aware Decision Support

Abstract

Personalized recommendation and context-aware decision support systems now shape a wide range of individual and institutional choices across commerce, media, healthcare, transportation, and public services. Although increasingly sophisticated models have improved predictive accuracy, their opacity frequently undermines user trust, regulatory compliance, and the capacity for meaningful contestability. This paper presents a system-level analysis of explainable artificial intelligence for personalized recommendation and context-aware decision support. It argues that explanation should not be treated merely as a post hoc interface feature or a model approximation tool, but rather as a first-class property embedded across data governance, contextual modeling, model training, inference, interaction design, and organizational oversight. The discussion examines structural trade-offs between explanation fidelity, computational cost, latency, privacy, fairness, and long-term system sustainability. It contrasts model-agnostic explanation strategies with inherently interpretable architectures and considers how context representation affects the legibility of recommendations. The paper further explores fairness and accountability as sociotechnical challenges that cannot be resolved through technical explanation alone. By examining cross-domain deployment patterns and emerging regulatory expectations, the analysis develops a governance-oriented perspective on explainable recommendation systems. It concludes that robust and fair explainable personalization requires coordinated design across machine learning pipelines, human-centered evaluation, operational infrastructure, and policy frameworks. This perspective moves beyond local explanation methods toward institutional arrangements that sustain contestability, auditability, and responsible automation in personalized decision environments.