Session-based recommendation predicts users' future interests from previous interactions in a session. Despite the memorizing of historical samples, the request of unlearning, i.e., to remove the effect of certain training samples, also occurs for reasons such as user privacy or model fidelity. However, existing studies on unlearning are not tailored for the session-based recommendation. On the one hand, these approaches cannot achieve satisfying unlearning effects due to the collaborative correlations and sequential connections between the unlearning item and the remaining items in the session. On the other hand, seldom work has conducted the research to verify the unlearning effectiveness in the session-based recommendation scenario. In this paper, we propose SRU, a session-based recommendation unlearning framework, which enables high unlearning efficiency, accurate recommendation performance, and improved unlearning effectiveness in session-based recommendation. Specifically, we first partition the training sessions into separate sub-models according to the similarity across the sessions, then we utilize an attention-based aggregation layer to fuse the hidden states according to the correlations between the session and the centroid of the data in the sub-model. To improve the unlearning effectiveness, we further propose three extra data deletion strategies, including collaborative extra deletion (CED), neighbor extra deletion (NED), and random extra deletion (RED). Besides, we propose an evaluation metric that measures whether the unlearning sample can be inferred after the data deletion to verify the unlearning effectiveness. We implement SRU with three representative session-based recommendation models and conduct experiments on three benchmark datasets. Experimental results demonstrate the effectiveness of our methods.

翻译：会话推荐通过会话中的历史交互预测用户的未来兴趣。尽管模型需要记忆历史样本，但因用户隐私或模型保真度等原因，也需要实现遗忘需求——即消除特定训练样本的影响。然而，现有遗忘研究尚未针对会话推荐场景进行适配。一方面，由于遗忘项与会话中剩余项之间存在协同关联与序列依赖关系，现有方法无法实现令人满意的遗忘效果；另一方面，鲜有研究对会话推荐场景下的遗忘有效性进行验证。本文提出SRU——一个面向会话推荐的遗忘框架，该框架在实现高遗忘效率、准确推荐性能的同时，显著提升了会话推荐中的遗忘有效性。具体而言，我们首先根据会话间的相似性将训练会话划分为独立子模型，随后利用基于注意力的聚合层，根据会话与子模型数据质心间的关联性融合隐状态。为增强遗忘效果，我们进一步提出三种额外数据删除策略：协同额外删除（CED）、邻居额外删除（NED）和随机额外删除（RED）。此外，我们设计了评估指标以衡量数据删除后能否推断出遗忘样本，从而验证遗忘有效性。我们基于三种代表性会话推荐模型实现SRU，并在三个基准数据集上进行实验。实验结果证明了我们方法的有效性。