A Privacy Preserving Personalized Search and Recommendation System Using Federated Learning and Web Usage Mining

Abstract

Traditional recommendation systems rely heavily on centralized data collection, where vast amounts of user interaction data are stored and processed on remote servers. Although such systems achieve high personalization accuracy, they introduce serious privacy risks, including data breaches, unauthorized profiling, and regulatory non-compliance. With increasing concerns surrounding data protection regulations such as GDPR and CCPA, there is a growing need for privacy-preserving recommendation mechanisms that maintain performance without compromising user confidentiality. This paper proposes a privacy-preserving personalized search and recommendation system based on federated learning and web mining techniques. Unlike traditional architectures, the proposed system ensures that user interaction data such as click frequency and browsing duration remains stored locally on client devices. Instead of transmitting raw behavioral data to a central server, each client trains a local machine learning model and shares only encrypted model weight updates. A secure aggregation mechanism based on federated averaging combines these encrypted weights at the server to generate a global model, which is then redistributed to clients for improved personalization.