IoT-Driven Hybrid Deep Collaborative Transformer with Federated Learning for Personalized E-Commerce Recommendations: An Optimized Approach

Abstract

Recommender systems are already being used by several biggest e-commerce websites to assist users in finding things to buy. A recommender system gains knowledge from a consumer and suggests goods from the available goods that will find most value. In this deep learning technique, the Hybrid Deep Collaborative Transformer (HDCT) method has emerged as a promising approach. However, it is crucial to thoroughly examine and rectify any potential errors or limitations in the optimization process to ensure the optimal performance of the HDCT model. This study aims to address this concern by thoroughly evaluating the HDCT method uncovering any underlying errors or shortcomings. By comparing its performance against other existing models, the proposed HDCT with Federated Learning method demonstrates superior recommendation accuracy and effectiveness. Through a comprehensive analysis, this research identifies and rectifies the errors in the HDCT model, thereby enhancing its overall performance. The findings of this study provide valuable insights for researchers and practitioners in the field of e-commerce recommendation systems. Data for the RS is collected from the Myntra fashion product dataset. By understanding and addressing the limitations of the HDCT method, businesses can leverage its advantages to improve customer satisfaction and boost their revenue. Ultimately, this research contributes to the ongoing advancements in e-commerce recommendation systems and paves the way for future improvements in this rapidly evolving domain. The suggested model’s efficacy is assessed using metrics for MSE, MSRE, NMSE, RMSE, and MAPE. The suggested values in metrics are 0.2971, 0.2763, 0.4013, 0.3222, 0.2911 at a 70% learn rate and 0.2403, 0.2234, 0.3506, 0.2025, 0.2597 at an 80% learn rate, and the proposed model outperformed with the least amount of error.