Knowledge-Driven Hybrid Models for E-Commerce Recommendations and Privacy

Abstract

The increasing reliance on E-Commerce has underscored the need for robust recommendation systems capable of delivering personalized and secure product suggestions. This research addresses the challenges of traditional models, such as data sparsity, scalability limitations, and privacy concerns, by introducing a hybrid deep learning framework that integrates Knowledge-Aware Neural Networks and Collaborative Filtering with private blockchain technology. Knowledge-Aware Neural Networks utilize knowledge graphs to encode complex relationships among products, users, and their attributes, while Collaborative Filtering captures latent patterns in user-item interactions to enhance prediction accuracy. We implemented private blockchain to ensure secure data handling, which aided in protecting user privacy through decentralized and tamper-resistant mechanisms. The system was evaluated using precision, recall, F1 score, and mean squared error, demonstrating superior performance compared to baseline models and achieving a 15% improvement in accuracy and enhanced data security. This research bridges significant gaps between recommendation systems, advanced deep learning techniques, and blockchain technology, offering practical applications for E-Commerce platforms to improve user engagement and trust. Future research may expand on this framework by incorporating real-time user feedback and adapting the model to other high-dimensional data domains, contributing further to the field's theoretical and practical advancements.