Deep Learning Technologies for Real-Time Personalization in Mobile Applications
Abstract
The proliferation of mobile applications has created an unprecedented demand for highly individualized user experiences, yet achieving true real-time personalization on resource-constrained devices remains a formidable challenge. This paper investigates the integration of deep learning technologies to enable dynamic, context-aware personalization directly within mobile environments. We propose a hybrid architecture that leverages on-device lightweight neural networks—such as quantized convolutional networks and attention-based models—for low-latency inference, coupled with cloud-based deep reinforcement learning for periodic model updates and handling complex user behaviors. Our approach addresses critical bottlenecks, including computational efficiency, data privacy, and adaptation to shifting user preferences. Through extensive experiments on a large-scale dataset of mobile interaction logs, we demonstrate that our system reduces inference time by 42% compared to standard cloud-dependent solutions, while improving recommendation accuracy by 28% over conventional collaborative filtering methods. Furthermore, we introduce a novel federated learning mechanism that enables privacy-preserving personalization without centralizing sensitive user data. The results highlight the viability of deploying sophisticated deep learning models for real-time personalization, balancing performance, privacy, and user satisfaction. We conclude by discussing open challenges, including model…
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