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This paper introduces a privacy-preserving recommendation framework that integrates federated learning, differential privacy, cohort-level modeling, and intelligent agents to maintain user data decentralization while ensuring compliance with privacy regulations like GDPR. Through experiments on synthetic retail datasets, the authors demonstrate that the framework can achieve competitive recommendation quality, even at moderate privacy budgets, by evaluating various recommendation techniques including matrix factorization and GRU4Rec. The findings indicate that strong privacy guarantees can be attained with minimal impact on recommendation effectiveness, paving the way for scalable AI-driven retail solutions.
Strong privacy guarantees in recommendation systems can be achieved with only a modest decrease in recommendation quality, challenging the notion that privacy and personalization are mutually exclusive.
Personalized recommendation systems are central to modern e-commerce and retail platforms, but they typically rely on centralized storage of detailed user interaction data, creating significant privacy and regulatory challenges. With increasing requirements from regulations such as GDPR, CCPA, and CPRA, organizations must develop recommendation systems that preserve user privacy without substantially degrading recommendation quality. This work presents and evaluates a privacy-preserving recommendation framework that combines federated learning, differential privacy, cohort-level modeling, and privacy-aware intelligent agents. The framework keeps raw user data decentralized while introducing mathematically bounded noise to model updates. Experiments were conducted on synthetic retail datasets that emulate customer clickstream and purchase behavior. Recommendation quality was evaluated using Click-Through Rate (CTR), Precision@K, Recall@K, and Normalized Discounted Cumulative Gain (NDCG@K) across multiple differential privacy budgets. We evaluate matrix factorization, neural collaborative filtering, and GRU4Rec under varying privacy constraints and analyze the trade-off between privacy and utility. An interactive Streamlit dashboard was developed to visualize recommendation performance, ranking stability, privacy-utility trade-offs, and fairness metrics. Results show that the proposed framework maintains competitive recommendation quality at moderate privacy budgets (approximately $\epsilon \approx 5$), demonstrating that strong privacy guarantees can be achieved with limited impact on recommendation effectiveness. This work provides a practical framework for deploying privacy-preserving recommendation systems that balance personalization, regulatory compliance, and business objectives, offering a scalable approach for next-generation AI-driven retail platforms.