Rethinking Privacy Protection for Recommender System in a Collaborative Way

Recommender systems play a crucial role in personalizing user experiences by analyzing vast amounts of user interaction records to suggest relevant items. However, the use of sensitive user information raises significant privacy concerns, particularly in light of regulations such as the General Data Protection Regulation. In response to these challenges, this paper introduces Rapid Collaborative Machine Unlearning (RaCoMU), a novel framework designed to efficiently handle user data deletion requests while preserving recommendation quality. RaCoMU employs a two-step data partitioning strategy that consolidates user data within individual subsets, enhancing collaborative information utilization. Additionally, a collaboration-based attention aggregation method is proposed, which weights contributions of the submodels based on user similarities. Our extensive experiments on real-world datasets across three recommendation models demonstrate that the proposed RaCoMU achieves superior unlearning efficiency and significantly outperforms existing frameworks in model effectiveness. The findings underscore the potential of RaCoMU to balance user privacy and recommendation performance effectively.