Lightweight Deep Learning Models for Personalized Promotions in FoodTech

Abstract

The rapid evolution of FoodTech, driven by the proliferation of digital platforms and mobile applications, has profoundly transformed how consumers discover, purchase, and interact with food services. Personalized promotions have emerged as a critical differentiator for FoodTech companies striving to retain customers, enhance engagement, and optimize revenue streams. However, achieving such personalized recommendations requires computationally efficient models, especially when operating on resource-constrained devices like smartphones and edge devices. This paper explores lightweight deep learning models tailored for delivering personalized promotions in the FoodTech sector. We review the state-of-the-art in recommendation systems, emphasizing the delicate trade-off between model complexity, latency, and inference speed. A comparative statistical analysis of various lightweight architectures—including MobileNet, SqueezeNet, and TinyBERT—demonstrates their suitability for personalized marketing tasks without sacrificing significant accuracy. We propose a methodology combining model pruning, knowledge distillation, and quantization to maintain high predictive performance while reducing resource usage. Experimental results reveal that MobileNetV3, when fine-tuned for user preference prediction, offers a 15% latency reduction while preserving over 93% of full-model accuracy, highlighting its practicality for real-time mobile applications.

Furthermore, the study underscores the importance of integrating diverse user data—including demographics, behavioral patterns, and contextual signals—to refine personalization strategies. It also discusses potential privacy concerns and the need for privacy-preserving technologies such as federated learning. Our findings demonstrate that lightweight deep learning models enable scalable, efficient, and privacy-conscious personalized promotions, significantly enhancing user satisfaction and FoodTech profitability. Ultimately, this research aims to bridge the gap between high-performance AI models and the practical limitations of mobile and edge computing environments, paving the way for the next generation of intelligent FoodTech solutions that can respond swiftly to user preferences while minimizing infrastructure costs. Future directions include the exploration of multimodal data fusion, reinforcement learning for adaptive promotions, and sustainability considerations in model deployment.