Published Paper Details:

This study presents a scalable and efficient hybrid recommendation framework designed to address the challenges of modern e-commerce systems, including data sparsity, cold-start problems, and real-time processing requirements. The proposed system integrates multiple recommendation strategies, namely content-based filtering, collaborative filtering, and deep learning-based approaches, to leverage their complementary strengths. User interaction data, such as clicks, views, and ratings, is collected and processed through a structured pipeline involving data storage, preprocessing, and feature engineering. The framework employs content-based techniques for feature extraction, collaborative filtering for user-item similarity modeling, and advanced methods such as matrix factorization and neural networks, including recurrent and transformer-based architectures, to capture complex interaction patterns. A hybrid filtering mechanism combines these approaches to generate highly personalized and context-aware recommendations. The system is further evaluated using standard performance metrics such as precision, recall, and RMSE, along with A/B testing to validate its effectiveness in real-world scenarios. Additionally, the proposed architecture supports scalable deployment through API-based services and enables real-time recommendation generation. A continuous feedback loop is incorporated to refine model performance based on user interactions. Experimental observations indicate that the hybrid approach improves recommendation accuracy, diversity, and robustness compared to traditional baseline models. Overall, the framework enhances user engagement and satisfaction while providing a practical solution for large-scale recommendation systems in competitive e-commerce environments.