计算机科学
频域
水准点(测量)
人工智能
正规化(语言学)
透视图(图形)
领域(数学分析)
时频分析
机器学习
航程(航空)
数据挖掘
电信
数学
数学分析
雷达
材料科学
大地测量学
复合材料
计算机视觉
地理
作者
Xiaolong Du,Huanhuan Yuan,Pengpeng Zhao,Jianfeng Qu,Fuzhen Zhuang,Guanfeng Liu,Yanchi Liu,Victor S. Sheng
标识
DOI:10.1145/3539618.3591689
摘要
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.
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