E-Commerce Products Recommendation System using Machine Learning

Abstract

This research focuses on the development and imple- mentation of an E-commerce product recommendation system us- ing machine learning techniques to enhance user experience. The system provides personalized product recommendations based on user preferences and product characteristics. By leveraging various algorithms, including Rating-based, Collaborative Filter- ing, Content-based Filtering, and a Hybrid Model approach, the system analyzes user behavior and product attributes to generate accurate and relevant recommendations. The implementation includes a web application for user interaction, along with performance evaluation metrics such as accuracy, precision, recall, F1-Score, MAE, and RMSE. The goal is to offer a comprehensive solution for e-commerce platforms to improve customer satisfaction and drive sales.