Architectures and Optimization Strategies for Real-Time Machine Learning Recommendation Systems: A Systematic Review of Scalability Challenges

Abstract

This article comprehensively analyzes the challenges and solutions in deploying real-time machine learning recommendation systems at scale. The article examines the critical trade-offs between model complexity, inference latency, and system scalability that impact modern recommendation architectures. The article investigates three primary dimensions: infrastructure optimization, model serving strategies, and resource utilization patterns. The article proposes a novel framework for balancing these competing requirements through a combination of distributed computing architectures, hybrid model deployment approaches, and intelligent caching mechanisms. The findings demonstrate that implementing a multi-tiered serving architecture with dynamic resource allocation significantly improves system performance while maintaining recommendation quality. The article also explores emerging optimization techniques, including model quantization, feature store architectures, and adaptive serving strategies. The article contributes to the field by providing a systematic approach to designing and implementing real-time recommendation systems that can effectively handle high-concurrency workloads while delivering personalized suggestions within strict latency constraints. The results offer valuable insights for practitioners and researchers working on large-scale recommendation systems, particularly in environments where real-time performance is crucial.