USING ARTIFICIAL INTELLIGENCE TO PERSONALIZE MOBILE APPLICATIONS

Abstract

Abstract. The article is an investigation into the current state of the art in applying AI techniques for personalizing mobile applications in the course of the evolution of mobile computing platforms and increasing requirements on confidentiality and adaptability of digital services. The author highlights the need for a personal user experience based on individual behavioral profiles, context information, scenarios of local interactions, and device constraints. Existing techniques for mobile personalization, such as machine learning and deep learning approaches, recommendation engines, NLP, real, time analytics, and hybrid methods of adapting content are reviewed.
Much attention is given to the issues of performance and resource limitations of mobile systems, and specifics of local model optimization. The impact of the hardware structure (processors) on the inference speed, energy efficiency, stability of personalization features in on, device mode is studied. It is demonstrated that it is precisely the presence of specialized neural processors that allows running a complex AI model without using cloud technology and at low processing latency, guaranteeing data security.
The paper talks about privacy problems and study the privacy, preserving personalization framework such as federated learning and differential privacy. It has been proven that the aggregation of local learning, local noisy mixing mechanism and optimization of neural models can provide private personalized services. The characteristics of the new generation of mobile artificial intelligence ecosphere is considered and their use for integrating personalized functionality, fast local model execution and creating adaptive user interface is identified.