The Adaptive Personalization Engine (APE): A Privacy-Respecting Deep Learning Framework for U.S. Smart TV User Engagement
Year: 2024
Type: article
Abstract: The Adaptive Personalization Engine (APE) presents a novel, privacy-respecting deep learning framework designed to enhance user engagement on smart TVs through on-device content personalization. By leveraging federated learning and edge-based anonymized AI, APE overcomes the limitations of traditional centralized recommendation systems—such as privacy risks, latency, and scalability challenges—while maintaining high personalization accuracy. The architecture integrates lightweight recurrent neural networks optimized for embedded devices, ensuring real-time responsiveness and efficient resource usage. Privacy and security are enforced through differential privacy mechanisms, data minimization, and encrypted communication, aligning with U.S. Federal Trade Commission guidelines and emerging ethical AI principles. Experimental results demonstrate that APE achieves comparable or superior recommendation accuracy to centralized models, with reduced latency and enhanced user engagement, all while preserving strong privacy guarantees. This framework advances domestic AI innovation and promotes responsible and ethical personalization technologies, serving as a blueprint for future scalable, privacy-conscious AI applications in consumer electronics.
Authors: Lamin Saidy, Abraham Ayodeji Abayomi, Abel Chukwuemeke Uzoka, Bolaji Iyanu Adekunle
Institutions: SKA Observatory, Vanguard (United States), Serco (United Kingdom)
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4410999901