通过频域分析捕捉用户兴趣的周期性，提升点击率预测精度。
FEDIN: Frequency-Enhanced Deep Interest Network for Click-Through Rate Prediction
- 引入频域分支，用目标感知滤波分离周期性兴趣信号。
- 在三个数据集上优于现有模型，对噪声更鲁棒。
- 适合做推荐系统中序列行为建模的研究者参考。
顺序推荐模型通常难以捕捉用户兴趣中隐含的周期性模式，主要因为时间域行为数据存在噪声。尽管频域分析能提供全局视角，但现有方法多孤立处理用户序列，忽略了目标商品的关键上下文。本文提出新观察：当目标商品为正样本或负样本时，用户注意力得分在频域表现出不同的谱熵分布。真实兴趣在频域呈现高度集中、熵值较低的模式，而无关行为则表现为高熵噪声。基于此，我们提出频域增强的兴趣网络（FEDIN），其频域分支采用目标感知谱滤波机制，有效提取周期性兴趣信号。在三个公开数据集上的大量实验表明，FEDIN持续优于当前最优的顺序推荐基线，对噪声具有更强鲁棒性。代码已开源：https://github.com/otokoneko/FEDIN。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。