Collaborative Filtering ML Technique for Recommendation in E-Commerce Energy System

Abstract

The growing integration of e-commerce into daily life has highlighted the essential need for recommender systems to enhance user experience and drive business success. This study examines machine learning techniques for developing effective recommender systems, with a particular focus on collaborative filtering (CF) and content-based filtering (CBF) methods. This study employs exploratory data analysis (EDA) to analyze e-commerce statistics, identifying trends in consumer behavior, product ratings, and user engagement, culminating in the development of a hybrid recommender system. The hybrid method amalgamates the benefits of Continuous Feedback (CF) with Context-Based Feedback (CBF) to deliver customized recommendations, hence improving accuracy and relevance for users. The application of hybrid models yields a notable improvement in predictive performance, evidenced by reduced mistake rates and heightened customer satisfaction. This research offers distinctive insights on user-product interactions, thereby advancing the comprehension of how machine learning could transform e-commerce personalization.