AI-Powered Collaborative Filtering for Hyper-Personalized: Leveraging Machine Learning and Business Intelligence

Abstract

This study offers a novel method for attaining hyper-personalization in e-commerce by combining business intelligence (BI) and machine learning technologies with collaborative filtering driven by artificial intelligence (AI). The main goal is to improve recommendation systems so that they can provide highly relevant and personalized product recommendations that suit the tastes and habits of specific users. The suggested method enhances recommendation accuracy and diversity by utilizing sophisticated deep learning architectures like convolutional and recurrent neural networks to record intricate user-item interaction patterns. Hybrid models are used to overcome issues like data sparsity and cold-start issues. These models combine content-based and collaborative filtering, and they make use of auxiliary data sources like social networks. Real-time data analysis, user segmentation, and performance monitoring are further made possible by the integration of BI tools, which promotes operational enhancements and strategic decision-making. According to experimental data, this all inclusive methodology performs noticeably better than conventional techniques, attaining greater precision, recall, and user engagement metrics. In the end, a strong, scalable solution that can offer a smooth, customized shopping experience, boost customer satisfaction, cultivate loyalty, and propel revenue growth in cutthroat e-commerce environments is produced by the synergistic use of AI, machine learning, and BI.