E-commerce platforms generate massive volumes of user interaction data, making personalized recommendation systems essential for enhancing user experience and increasing sales. This research focuses on the design and deployment of a cloud-based scalable recommendation system capable of delivering accurate and real-time product suggestions. The proposed system integrates collaborative filtering and content-based filtering techniques to analyse user behaviour, preferences, and product attributes. Cloud infrastructure is utilized to ensure high availability, scalability, and efficient handling of large datasets and concurrent users. Distributed data storage and parallel processing enable faster model training and recommendation generation. Performance evaluation is conducted using metrics such as accuracy, precision, and response time to validate system effectiveness. The results demonstrate that the cloud-based approach significantly improves scalability and recommendation efficiency compared to traditional standalone systems. This work highlights the importance of cloud computing in building robust, cost-effective, and scalable recommendation solutions for modern e- commerce platforms.

This project successfully presents the design and implementation of a cloud-based scalable recommendation system for e-commerce platforms. By integrating user interaction data such as clicks, searches, ratings, and purchase history, the system effectively generates personalized product recommendations. The use of collaborative filtering, content-based filtering, and hybrid recommendation techniques improves recommendation accuracy while addressing common challenges such as data sparsity and cold-start problems. The cloud infrastructure plays a vital role in ensuring scalability, reliability, and high availability, allowing the system to handle large volumes of users and data efficiently. Modular components such as data collection, processing, recommendation engine, and API services enable easy system maintenance and future enhancements. Continuous model evaluation and retraining help maintain recommendation quality as user preferences evolve over time. Overall, the proposed system demonstrates how cloud computing combined with machine learning can enhance user experience and business performance in e-commerce environments. The project provides a strong foundation for real-world deployment and can be further extended by incorporating deep learning models, real-time analytics, and advanced user behaviour tracking to achieve even more accurate and dynamic recommendations.

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