Sequential recommendation models have achieved state-of-the-art performance using self-attention mechanism. It has since been found that moving beyond only using item ID and positional embeddings leads to a significant accuracy boost when predicting the next item. In recent literature, it was reported that a multi-dimensional kernel embedding with temporal contextual kernels to capture users' diverse behavioral patterns results in a substantial performance improvement. In this study, we further improve the sequential recommender model's robustness and generalization by introducing a mix-attention mechanism with a layer-wise noise injection (LNI) regularization. We refer to our proposed model as adaptive robust sequential recommendation framework (ADRRec), and demonstrate through extensive experiments that our model outperforms existing self-attention architectures.

翻译：序列推荐模型通过自注意力机制已实现最先进的性能。研究发现，在预测下一项时，突破仅使用物品ID和位置嵌入的限制能显著提升预测精度。近期文献表明，采用具有时序上下文核的多维核嵌入来捕捉用户多样化的行为模式，可带来显著的性能提升。本研究通过引入混合注意力机制及分层噪声注入正则化，进一步增强了序列推荐模型的鲁棒性与泛化能力。我们将所提出的模型称为自适应鲁棒序列推荐框架，并通过大量实验证明该模型优于现有的自注意力架构。