Privacy Preserving Recommender Systems: Balancing Personalization with Privacy

Personalized recommendation systems have become an essential part of how modern digital products operate, especially in e-commerce and large retail platforms. These systems rely heavily on analyzing customer behavior, purchase signals, and historical interactions to deliver suggestions that feel relevant. In practice, however, most of these pipelines still depend on centralized storage of detailed customer data. As privacy expectations increase and laws such as CPRA(California Privacy Rights Act), CCPA(California Consumer Privacy Act) and GDPR (General Data Protection Regulation) become stricter, this approach is becoming harder to justify. Organizations now need to rethink how personalization can work without relying on the unrestricted collection of individual user data.