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.

翻译：随着人工智能领域隐私问题日益突出，相关法规已确立“被遗忘权”，赋予个人从模型中撤回其数据的权利。机器学习遗忘技术作为一种潜在解决方案应运而生，旨在实现模型的选择性遗忘，尤其在历史数据包含敏感用户信息的推荐系统中。尽管推荐遗忘领域近期取得进展，但由于缺乏统一的评估框架且忽视了深层影响（如公平性）的考量，全面评估遗忘方法仍面临挑战。为弥补这些不足，我们提出了CURE4Rec——首个用于推荐遗忘评估的综合基准。CURE4Rec涵盖遗忘完整性、推荐效用、遗忘效率与推荐公平性四个维度，并在核心数据、边缘数据及随机数据三种数据选择策略下进行评估。具体而言，我们考虑了遗忘对不同影响程度数据的推荐公平性与鲁棒性所产生的深层影响。我们基于CURE4Rec评估框架构建了多个数据集，并对现有推荐遗忘方法进行了广泛实验。相关代码已发布于 https://github.com/xiye7lai/CURE4Rec。