Sequential recommendation (SR) aims to model user preferences by capturing behavior patterns from their item historical interaction data. Most existing methods model user preference in the time domain, omitting the fact that users' behaviors are also influenced by various frequency patterns that are difficult to separate in the entangled chronological items. However, few attempts have been made to train SR in the frequency domain, and it is still unclear how to use the frequency components to learn an appropriate representation for the user. To solve this problem, we shift the viewpoint to the frequency domain and propose a novel Contrastive Enhanced \textbf{SLI}de Filter \textbf{M}ixEr for Sequential \textbf{Rec}ommendation, named \textbf{SLIME4Rec}. Specifically, we design a frequency ramp structure to allow the learnable filter slide on the frequency spectrums across different layers to capture different frequency patterns. Moreover, a Dynamic Frequency Selection (DFS) and a Static Frequency Split (SFS) module are proposed to replace the self-attention module for effectively extracting frequency information in two ways. DFS is used to select helpful frequency components dynamically, and SFS is combined with the dynamic frequency selection module to provide a more fine-grained frequency division. Finally, contrastive learning is utilized to improve the quality of user embedding learned from the frequency domain. Extensive experiments conducted on five widely used benchmark datasets demonstrate our proposed model performs significantly better than the state-of-the-art approaches. Our code is available at https://github.com/sudaada/SLIME4Rec.

翻译：序列推荐旨在通过用户历史交互数据中的行为模式来建模用户偏好。现有方法大多在时域中建模用户偏好，忽略了用户行为还受到各种频率模式的影响，而这些频率模式在纠缠的时间顺序项中难以分离。然而，目前鲜有尝试在频域中训练序列推荐模型，且仍不清楚如何利用频率分量学习合适的用户表示。为解决这一问题，我们将视角转向频域，提出了一种新颖的对比增强型滑动过滤器混合器用于序列推荐，命名为SLIME4Rec。具体而言，我们设计了一种频率斜坡结构，使可学习过滤器在不同层的频谱上滑动，以捕获不同的频率模式。此外，还提出了动态频率选择模块和静态频率分离模块，以替代自注意力模块，通过两种方式有效提取频率信息。动态频率选择用于动态选择有用的频率分量，静态频率分离则与动态频率选择模块结合，提供更细粒度的频率划分。最后，利用对比学习提升从频域学得的用户嵌入质量。在五个广泛使用的基准数据集上进行的大量实验表明，我们的模型性能显著优于现有最先进方法。相关代码已开源在https://github.com/sudaada/SLIME4Rec。