Sequential recommendation represents a pivotal branch of recommendation systems, centered around dynamically analyzing the sequential dependencies between user preferences and their interactive behaviors. Despite the Transformer architecture-based models achieving commendable performance within this domain, their quadratic computational complexity relative to the sequence dimension impedes efficient modeling. In response, the innovative Mamba architecture, characterized by linear computational complexity, has emerged. Mamba4Rec further pioneers the application of Mamba in sequential recommendation. Nonetheless, Mamba 1's hardware-aware algorithm struggles to efficiently leverage modern matrix computational units, which lead to the proposal of the improved State Space Duality (SSD), also known as Mamba 2. While the SSD4Rec successfully adapts the SSD architecture for sequential recommendation, showing promising results in high-dimensional contexts, it suffers significant performance drops in low-dimensional scenarios crucial for pure ID sequential recommendation tasks. Addressing this challenge, we propose a novel sequential recommendation backbone model, TiM4Rec, which ameliorates the low-dimensional performance loss of the SSD architecture while preserving its computational efficiency. Drawing inspiration from TiSASRec, we develop a time-aware enhancement method tailored for the linear computation demands of the SSD architecture, thereby enhancing its adaptability and achieving state-of-the-art (SOTA) performance in both low and high-dimensional modeling. The code for our model is publicly accessible at https://github.com/AlwaysFHao/TiM4Rec.

翻译：序列推荐是推荐系统的一个关键分支，其核心在于动态分析用户偏好与其交互行为之间的序列依赖关系。尽管基于Transformer架构的模型在该领域取得了值得称赞的性能，但其相对于序列维度的二次计算复杂度阻碍了高效建模。为此，具有线性计算复杂度特征的创新性Mamba架构应运而生。Mamba4Rec进一步开创了Mamba在序列推荐中的应用。然而，Mamba 1的硬件感知算法难以有效利用现代矩阵计算单元，这促使了改进版状态空间对偶（SSD，也称为Mamba 2）的提出。虽然SSD4Rec成功地将SSD架构适配于序列推荐，并在高维场景下展现出有希望的结果，但在对纯ID序列推荐任务至关重要的低维场景中，其性能却显著下降。针对这一挑战，我们提出了一种新颖的序列推荐骨干模型TiM4Rec，该模型在保持SSD架构计算效率的同时，改善了其在低维场景下的性能损失。受TiSASRec的启发，我们开发了一种专为SSD架构线性计算需求定制的时间感知增强方法，从而提升了其适应性，并在低维和高维建模中均实现了最先进的性能。我们模型的代码已在https://github.com/AlwaysFHao/TiM4Rec公开。