Federated cross-view e-commerce recommendation based on feature rescaling

Abstract As big data technologies continue to evolve, recommendation systems have found broad application in domains such as online retail and social networking platforms. However, centralized recommendation systems raise numerous data privacy concerns. Federated learning addresses these concerns by allowing model training on client devices and aggregating model parameters without sharing raw data. Nevertheless, federated learning faces critical challenges related to feature extraction efficiency and noise sensitivity, limiting its application in e-commerce recommendation systems where data heterogeneity and high-dimensional features are prevalent. To address these gaps, this paper introduces a novel multi-view federated learning framework, Fed-FR-MVD, designed to enhance feature extraction efficiency and improve recommendation accuracy in e-commerce applications. Fed-FR-MVD integrates a FR mechanism within a multi-view structure, incorporating both item and user perspectives to improve feature representation and robustness. This approach yields a 12%–18% increase in recommendation accuracy across various performance metrics compared to single-view and other multi-view methods. By addressing data heterogeneity and optimizing feature utilization through dynamic rescaling, Fed-FR-MVD effectively mitigates the impact of noisy data, with performance maintained across noise levels of 5%–15%. Experimental results demonstrate that Fed-FR-MVD fills a key research gap by providing a more resilient and efficient framework for federated recommendation systems in privacy-sensitive and data-diverse e-commerce environments.