Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems

TL;DR

This paper presents combined modeling and system-level strategies to significantly reduce inference latency and boost throughput in real-time recommendation systems without losing accuracy.

Contribution

It introduces a comprehensive approach integrating lightweight models, pruning, quantization, and system optimization for efficient large-scale recommendation deployment.

Findings

Latency reduced to less than 30% of baseline

System throughput more than doubled

Recommendation accuracy maintained

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.