Design and Implementation of an AI-Driven Recommendation System for Online Platforms
Abstract
The proliferation of digital content has transformed recommendation systems from peripheral features into the central nervous systems of modern online platforms. This paper presents a comprehensive, interdisciplinary analysis of the design and implementation of artificial intelligence-driven recommendation systems, moving beyond traditional algorithmic accuracy to explore the systemic complexities of large-scale socio-technical infrastructures. We examine the architectural shift from monolithic collaborative filtering to decentralized, deep-learning-based frameworks capable of processing multi-modal data streams in real-time. The research emphasizes the critical structural trade-offs between predictive precision, computational efficiency, and system robustness. Central to our discussion is the emergence of systemic fairness and the ethical governance required to mitigate algorithmic bias and filter bubbles that threaten social cohesion. Furthermore, the paper investigates the physical and digital infrastructure necessary to sustain these systems, addressing the environmental impact of high-frequency model retraining and the policy implications of data sovereignty in a globalized digital economy. By synthesizing perspectives from engineering, behavioral science, and public policy, this article provides a holistic framework for deploying recommendation engines that are not only technologically superior but also socially responsible and environmentally sustainable. The study concludes with a forward-looking roadmap for the next generation of recommendation systems, emphasizing the integration of explainable AI and human-centric design to ensure long-term platform resilience and user trust.
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