Sequential recommendation models often struggle to capture latent periodic patterns in user interests, primarily due to the noise inherent in time-domain behavioral data. While frequency-domain analysis offers a global perspective to address this, existing approaches typically treat user sequences in isolation, overlooking the crucial context of the target item. In this work, we present a novel empirical observation: user attention scores exhibit distinct spectral entropy distributions when conditioned on positive versus negative target items. Specifically, true user interests manifest as highly concentrated spectral patterns with lower entropy in the frequency domain, whereas irrelevant behaviors appear as high-entropy noise. Leveraging this insight, we propose the Frequency-Enhanced Deep Interest Network (FEDIN). FEDIN introduces a frequency-domain branch that utilizes a target-aware spectrum filtering mechanism to isolate these periodic interest signals. Extensive experiments on three public datasets demonstrate that FEDIN consistently outperforms state-of-the-art sequential recommendation baselines, demonstrating superior robustness against noise. We have released our code at: https://github.com/otokoneko/FEDIN.

翻译：序列推荐模型常因时域行为数据中的噪声干扰，难以捕获用户兴趣中潜在的周期模式。尽管频域分析能从全局视角解决该问题，但现有方法通常独立处理用户序列，忽略了目标物品的关键上下文。本文提出一项新颖的经验发现：当以正、负目标物品为条件时，用户注意力得分在频谱熵分布上呈现显著差异。具体而言，真实用户兴趣在频域中表现为高度集中的低熵频谱模式，而无关行为则呈现为高熵噪声。基于此发现，我们提出频率增强深度兴趣网络（FEDIN）。该模型引入频域分支，通过目标感知频谱过滤机制分离周期性兴趣信号。在三个公开数据集上的大量实验表明，FEDIN在噪声鲁棒性方面持续优于现有最先进的序列推荐基线模型。我们已公开源代码：https://github.com/otokoneko/FEDIN。