CURE4Rec: A Benchmark for Recommendation Unlearning with Deeper Influence
This work addresses the need for standardized evaluation in recommendation unlearning to support privacy regulations, but it is incremental as it builds on existing methods without introducing new unlearning techniques.
The authors tackled the lack of a unified evaluation framework for recommendation unlearning by proposing CURE4Rec, a comprehensive benchmark that assesses unlearning completeness, utility, efficiency, and fairness across multiple datasets and strategies.
With increasing privacy concerns in artificial intelligence, regulations have mandated the right to be forgotten, granting individuals the right to withdraw their data from models. Machine unlearning has emerged as a potential solution to enable selective forgetting in models, particularly in recommender systems where historical data contains sensitive user information. Despite recent advances in recommendation unlearning, evaluating unlearning methods comprehensively remains challenging due to the absence of a unified evaluation framework and overlooked aspects of deeper influence, e.g., fairness. To address these gaps, we propose CURE4Rec, the first comprehensive benchmark for recommendation unlearning evaluation. CURE4Rec covers four aspects, i.e., unlearning Completeness, recommendation Utility, unleaRning efficiency, and recommendation fairnEss, under three data selection strategies, i.e., core data, edge data, and random data. Specifically, we consider the deeper influence of unlearning on recommendation fairness and robustness towards data with varying impact levels. We construct multiple datasets with CURE4Rec evaluation and conduct extensive experiments on existing recommendation unlearning methods. Our code is released at https://github.com/xiye7lai/CURE4Rec.