Sarcouncil Journal of Engineering and Computer Sciences

Abstract: Modern e-commerce platforms are very reliant on advanced recommendation engines to successfully engage users, drive sales, and generate revenue. The evolution from traditional batch processing systems to a real-time personalization processing environment introduces many technical challenges requiring a holistic solution across different layers of the system. Vector databases provide key functionality for similarity search and embedding storage; they allow easy access to contextual similarity to a user’s collaborators – whether it's a friend, peer, or family. There is a Pragmatic Technologies-based method to real-time user embedding generation that enables real-time recommendations by adapting to user interactions as they happen. Edge inference deployment addresses latency scalability and also inhibits the delivery of timely recommendations, especially with collaboration with time-sensitive user interactions, such as stock price recommendations. Multi-model experimentation platforms allow development teams to evaluate new features by enabling experimental A/B testing frameworks and automated retraining pipelines to assess the merit of each variant. Cloud-based deployments are dynamic successively complex challenges, particularly cold start problems, including data consistency in a distributed system and cost-effectiveness for compute-intensive workloads. The evolution from legacy to batch-based systems includes new hybrid architectural patterns that enable organizations to develop new systems within the constraints of available services. Feature stores and orchestrated data pipelines must now support high-traffic, high-throughput personalization applications. Design decisions regarding infrastructure impact not only build performance but also production operational costs and the ability to deliver the most relevant recommendations within acceptable latency. These architectural choices form the foundation of successful large-scale recommendation systems and directly influence the diversity of recommendations a user receives, as their preferences evolve in near real-time.