Recommendation System Enhanced by Big Data Analytics

This research study describes the integration of big data analytics to develop a novel and robust e-commerce recommendation system. Acknowledging the challenges associated with providing accurate and timely suggestions, advanced recommendation algorithms, and the utilization of big data analytics tools. This study employs a comprehensive methodology, beginning with the characterization of the e-commerce dataset. As a first step, this study explains the data pre-processing steps undertaken to ensure the quality and relevance of information. The proposed recommendation system design incorporates state-of-the-art algorithms. Implementation details describe the practical aspects of deploying the recommendation system in an e-commerce environment, including the challenges encountered and their resolutions. The results section presents key findings, supported by visual representations and performance metrics that gauge the effectiveness of the proposed system. Through a thoughtful discussion, this study contextualizes the results within the broader landscape of e-commerce recommendation systems and big data analytics. Drawing comparisons with existing literature, this study highlights the significance of various research contributions and identify opportunities for future research. In conclusion, this research not only provides insights into optimizing e-commerce recommendations but also contributes to the ongoing research on leveraging big data analytics in enhancing user engagement and satisfaction.