A NOVEL DESIGN AND IMPLEMENTATION OF SCALABLE MACHINE LEARNING BASED PRODUCT SUGGESTION FRAMEWORK IN E-COMMERCE

Abstract

This study focuses on building an advanced recommendation system that uses machine learning to deliver more personalized and meaningful user experiences. The system combines different techniques, including collaborative filtering, content-based filtering, and a hybrid strategy, to generate more relevant and accurate recommendations across various application domains. A standard benchmark dataset was used to train and evaluate the model, while several machine learning algorithms were explored to determine the most effective approach. The results show a clear improvement in recommendation accuracy when compared with traditional methods. The findings emphasize the critical role of feature selection, similarity computation, and hybrid modeling in addressing the shortcomings of single-method systems. Overall, the proposed framework is designed to be scalable, flexible, and dependable, making it suitable for real-world environments such as e-commerce platforms, digital marketing systems, and other online services. In addition, the system is capable of adapting to changing user preferences over time, ensuring that recommendations remain relevant. It also reduces information overload by presenting users with tailored suggestions rather than generic content. The integration of multiple techniques helps overcome issues like data sparsity and cold-start problems. Furthermore, the model is designed with efficiency in mind, allowing it to handle large volumes of data without significant performance loss. The study also explores the importance of user behavior analysis in improving recommendation quality. By learning from user interactions, the system continuously refines its predictions. Another key contribution is the balance achieved between accuracy and computational cost, which is essential for practical deployment. The framework can be extended to incorporate deep learning models for further enhancement.Moreover, the proposed approach supports real-time recommendation generation, which is crucial for modern applications. It also ensures better user engagement and satisfaction by delivering context-aware suggestions. The flexibility of the system allows it to be customized for different industries and use cases. Finally, this research provides a strong foundation for future work in intelligent recommendation systems, encouraging further exploration of hybrid and adaptive machine learning techniques