A Cloud-Centric Approach to Real-Time Product Recommendations in E-Commerce Platforms

: In the rapidly evolving landscape of digital commerce, delivering personalized and context-aware product recommendations in real time has become a key differentiator for e-commerce platforms. Traditional recommender systems, while effective to an extent, often struggle with scalability, latency, and the dynamic nature of user behavior and inventory updates. This article presents a cloud-centric approach to designing and deploying real-time product recommendation systems tailored for modern e-commerce environments. Leveraging the elastic and distributed nature of cloud computing, the proposed framework enables scalable data ingestion, storage, and processing of vast and continuously growing datasets. Cloud services such as distributed data warehouses, serverless computing, and real-time analytics pipelines are integrated to support low-latency recommendation delivery across millions of users. The system architecture employs microservices for modular deployment, allowing independent scaling of components such as user behavior tracking, product metadata indexing, and machine learning model serving. At the core of the recommendation engine is a hybrid filtering technique that combines collaborative filtering, content-based filtering, and deep learning models to enhance accuracy and personalization. These models are trained and deployed using cloud-based machine learning platforms that support continuous learning through real-time data streams. The use of container orchestration platforms like Kubernetes further ensures high availability, fault tolerance, and seamless model updates without system downtime. A case study involving a mid-sized e-commerce retailer demonstrates the effectiveness of the proposed solution. Key performance metrics such as click-through rate (CTR), conversion rate, and average session duration are analyzed before and after the cloud migration. Results show a significant improvement in recommendation relevance, system responsiveness, and overall user engagement. Additionally, the cost-efficiency and flexibility of cloud resources highlight the viability of this approach for both startups and enterprise-level platforms. This article also addresses security and privacy concerns, discussing the implementation of secure APIs, data encryption, and compliance with regulations like GDPR. The findings emphasize that a cloud-centric recommendation engine not only meets current scalability and performance demands but also positions e-commerce platforms for future growth and innovation.

Cloud Computing, Recommender Systems, E-Commerce, Real-Time Recommendations, Collaborative Filtering, Content-Based Filtering, Hybrid Recommendation, Personalization, Machine Learning, Microservices Architecture, Kubernetes, Serverless Computing, User Engagement, Scalable Systems, Cloud-Based AI

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