Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems
Journal:
arXiv
Published Date:
Jun 13, 2025
Abstract
With the rapid growth of Internet services, recommendation systems play a
central role in delivering personalized content. Faced with massive user
requests and complex model architectures, the key challenge for real-time
recommendation systems is how to reduce inference latency and increase system
throughput without sacrificing recommendation quality. This paper addresses the
high computational cost and resource bottlenecks of deep learning models in
real-time settings by proposing a combined set of modeling- and system-level
acceleration and optimization strategies. At the model level, we dramatically
reduce parameter counts and compute requirements through lightweight network
design, structured pruning, and weight quantization. At the system level, we
integrate multiple heterogeneous compute platforms and high-performance
inference libraries, and we design elastic inference scheduling and
load-balancing mechanisms based on real-time load characteristics. Experiments
show that, while maintaining the original recommendation accuracy, our methods
cut latency to less than 30% of the baseline and more than double system
throughput, offering a practical solution for deploying large-scale online
recommendation services.
