The self-attention mechanism, which equips with a strong capability of modeling long-range dependencies, is one of the extensively used techniques in the sequential recommendation field. However, many recent studies represent that current self-attention based models are low-pass filters and are inadequate to capture high-frequency information. Furthermore, since the items in the user behaviors are intertwined with each other, these models are incomplete to distinguish the inherent periodicity obscured in the time domain. In this work, we shift the perspective to the frequency domain, and propose a novel Frequency Enhanced Hybrid Attention Network for Sequential Recommendation, namely FEARec. In this model, we firstly improve the original time domain self-attention in the frequency domain with a ramp structure to make both low-frequency and high-frequency information could be explicitly learned in our approach. Moreover, we additionally design a similar attention mechanism via auto-correlation in the frequency domain to capture the periodic characteristics and fuse the time and frequency level attention in a union model. Finally, both contrastive learning and frequency regularization are utilized to ensure that multiple views are aligned in both the time domain and frequency domain. Extensive experiments conducted on four widely used benchmark datasets demonstrate that the proposed model performs significantly better than the state-of-the-art approaches.

翻译：自注意力机制凭借其强大的长距离依赖建模能力，是序列推荐领域广泛使用的技术之一。然而，近期许多研究表明，基于自注意力的模型本质上是低通滤波器，难以捕捉高频信息。此外，由于用户行为中的物品相互交织，这些模型无法充分区分隐藏在时域中的固有周期性。在本文中，我们转向频域视角，提出了一种新颖的面向序列推荐的频率增强混合注意力网络——FEARec。在该模型中，我们首先利用斜坡结构在频域中改进原始时域自注意力，使低频和高频信息均能在我们的方法中被显式学习。此外，我们额外设计了一种基于频域自相关的类似注意力机制以捕捉周期性特征，并将时域与频域注意力融合于统一模型中。最后，采用对比学习和频率正则化确保多视角在时域和频域上对齐。在四个广泛使用的基准数据集上进行的大量实验表明，所提模型性能显著优于现有最先进方法。