Approximate unlearning for session-based recommendation refers to eliminating the influence of specific training samples from the recommender without retraining of (sub-)models. Gradient ascent (GA) is a representative method to conduct approximate unlearning. However, there still exist dual challenges to apply GA for session-based recommendation. On the one hand, naive applying of GA could lead to degradation of recommendation performance. On the other hand, existing studies fail to consider the ordering of unlearning samples when simultaneously processing multiple unlearning requests, leading to sub-optimal recommendation performance and unlearning effect. To address the above challenges, we introduce CAU, a curriculum approximate unlearning framework tailored to session-based recommendation. CAU handles the unlearning task with a GA term on unlearning samples. Specifically, to address the first challenge, CAU formulates the overall optimization task as a multi-objective optimization problem, where the GA term for unlearning samples is combined with retaining terms for preserving performance. The multi-objective optimization problem is solved through seeking the Pareto-Optimal solution, which achieves effective unlearning with trivial sacrifice on recommendation performance. To tackle the second challenge, CAU adopts a curriculum-based sequence to conduct unlearning on batches of unlearning samples. The key motivation is to perform unlearning from easy samples to harder ones. To this end, CAU first introduces two metrics to measure the unlearning difficulty, including gradient unlearning difficulty and embedding unlearning difficulty. Then, two strategies, hard-sampling and soft-sampling, are proposed to select unlearning samples according to difficulty scores.

翻译：面向会话推荐的近似遗忘学习旨在无需重新训练（子）模型的情况下，从推荐系统中消除特定训练样本的影响。梯度上升法是一种进行近似遗忘学习的代表性方法。然而，将梯度上升法应用于会话推荐仍面临双重挑战。一方面，直接应用梯度上升法可能导致推荐性能下降。另一方面，现有研究在处理多个遗忘请求时，未能考虑遗忘样本的顺序，导致次优的推荐性能和遗忘效果。为解决上述挑战，我们提出了CAU，一个专为会话推荐设计的课程式近似遗忘学习框架。CAU通过一个作用于遗忘样本的梯度上升项来处理遗忘任务。具体而言，针对第一个挑战，CAU将整体优化任务构建为一个多目标优化问题，其中遗忘样本的梯度上升项与用于保持性能的保留项相结合。该多目标优化问题通过寻求帕累托最优解来解决，从而以微小的推荐性能牺牲实现有效的遗忘。为应对第二个挑战，CAU采用基于课程的序列来对批量的遗忘样本进行遗忘学习。其核心动机是从易到难地执行遗忘。为此，CAU首先引入了两个指标来衡量遗忘难度，包括梯度遗忘难度和嵌入遗忘难度。然后，提出了两种策略——硬采样和软采样——以根据难度分数来选择遗忘样本。