Sequential recommendation involves automatically recommending the next item to users based on their historical item sequence. While most prior research employs RNN or transformer methods to glean information from the item sequence-generating probabilities for each user-item pair and recommending the top items, these approaches often overlook the challenge posed by spurious relationships. This paper specifically addresses these spurious relations. We introduce a novel sequential recommendation framework named Irl4Rec. This framework harnesses invariant learning and employs a new objective that factors in the relationship between spurious variables and adjustment variables during model training. This approach aids in identifying spurious relations. Comparative analyses reveal that our framework outperforms three typical methods, underscoring the effectiveness of our model. Moreover, an ablation study further demonstrates the critical role our model plays in detecting spurious relations.

翻译：序列推荐涉及根据用户的历史项目序列自动推荐下一个项目。尽管大多数先前研究使用RNN或Transformer方法从项目序列中提取信息，生成每个用户-项目对的概率并推荐排名靠前的项目，但这些方法常常忽略了虚假关系带来的挑战。本文专门针对这些虚假关系进行了探讨。我们提出了一种名为Irl4Rec的新型序列推荐框架。该框架利用不变学习，并采用一个考虑模型训练过程中虚假变量与调整变量之间关系的新目标函数。这种方法有助于识别虚假关系。对比分析表明，我们的框架优于三种典型方法，突显了模型的有效性。此外，消融研究进一步证明了我们的模型在检测虚假关系中的关键作用。