An Efficient Unified System for Product Rating Prediction and Content-based Personalized Recommendations Using Amazon Data

This paper introduces an efficient, unified system for product rating prediction and content-based personalized recommendations using Amazon data. With the surge in online shopping, accurately predicting user ratings and delivering personalized recommendations are essential for enhancing customer experiences and boosting sales. Our system leverages machine learning and natural language processing to analyze Amazon’s extensive datasets, including product reviews, descriptions, and user profiles. Our innovative approach, which concentrates on content-based methods to predict product ratings and generate recommendations, has been validated through experimental evaluations to significantly improve prediction accuracy and recommendation quality compared to traditional methods. This unified system provides a robust solution to the challenges of rating prediction and personalized recommendations in the e-commerce sector. Overall, the support vector machine (SVM) and random forest (RFT) performed well compared to other existing models. It delivers tangible benefits to consumers and sellers on the Amazon platform, fostering a more efficient and satisfying shopping experience.