Enhancing E-Commerce Recommendations Through Data-Driven Approaches: A Case Study of Amazon Product Reviews

Abstract

In the dynamic landscape of e-commerce, personalized product recommendation systems are pivotal in enhancing user experiences and driving business success. This study leverages the Amazon Product Reviews dataset, a rich source of user-generated feedback, to design a scalable and effective personalized recommendation system. Adopting a structured methodology encompassing four data analysis phases includes descriptive, diagnostic, predictive, and prescriptive. This research extracts meaningful insights from product reviews and ratings. The study captures user preferences and sentiments using advanced natural language processing (NLP) and machine learning techniques, including sentiment analysis and hybrid recommendation models. Implementing distributed computing frameworks like Apache Spark ensures scalability and operational efficiency. Centered on the electronics category, this research integrates sentiment insights with collaborative and content-based filtering techniques to address challenges like data sparsity and the cold-start problem. The findings contribute to advancing personalized recommendation systems by delivering actionable insights that enhance customer satisfaction, streamline product discovery, and provide significant value to academic research and industry practices.