Edge‐Optimized Neural Networks for Real‐Time English Corpus Recommendation

Abstract

This paper presents a lightweight framework for real‐time English corpus recommendation optimized for edge computing environments. Traditional recommendation systems, though effective in modeling complex user‐content interactions, often suffer from excessive computational demands, making them unsuitable for deployment on resource‐constrained edge devices. To tackle these limitations, we introduce a novel solution that efficiently balances advanced interaction modeling with computational cost optimization. The framework integrates a robust baseline model that captures rich user‐content interactions while employing knowledge distillation techniques to transfer critical information, ensuring that high‐quality recommendations are maintained even with reduced computational overhead. Our approach leverages real‐time adaptability to edge environments, providing both speed and accuracy. Experimental evaluations on benchmark datasets reveal significant reductions in response time, without compromising the accuracy of recommendations, thus making it ideal for real‐time applications in edge computing scenarios.