Privacy Preserving Recommender Systems: Balancing Personalization with Privacy

Modern e-commerce platforms rely on centralized user data to fuel personalized recommendations, creating significant privacy risks and regulatory challenges under laws like GDPR and CCPA. This paper investigates whether it is possible to maintain high-quality recommendations while decentralizing data and applying mathematical privacy guarantees. The authors propose a framework that integrates federated learning, differential privacy (DP), and cohort-level modeling to protect user data without sacrificing performance.

To test this, the researchers used synthetic retail datasets mimicking customer clickstream and purchase behaviors. They evaluated three common recommendation architectures—Matrix Factorization (MF), Neural Collaborative Filtering (NCF), and GRU4Rec—under varying privacy constraints. The system was designed to keep raw data on-device, with only noisy, aggregated model updates sent to a central server.

The experiments reveal that recommendation quality remains robust at a moderate privacy budget (epsilon ≈ 5). The authors observed that while adding noise generally degrades performance, the impact is manageable. Specifically, they found that simpler models like Matrix Factorization were more stable under DP-SGD training compared to complex neural architectures like NCF, which exhibited higher volatility as noise compounded across layers. The use of cohort-level modeling—grouping users into behavioral segments—provided a natural privacy buffer and improved generalization, particularly for sparse datasets where individual user history is limited.

This work offers a practical blueprint for organizations struggling to balance the business necessity of personalization with the legal and ethical mandates of data privacy. By demonstrating that strong privacy guarantees can be achieved alongside competitive performance metrics (such as CTR and NDCG), the authors provide a scalable, future-ready approach for deploying AI-driven retail systems that respect user autonomy and reduce the risks associated with centralized data storage.