AI 中文总结
本研究针对会话式推荐的零星噪声问题，提出结合权重融合与FFT滤波的全MLP框架DTAMLP，在Diginetica和RetailRocket数据集上验证了两种机制的互补改进效果。
AI 中文摘要
本文报告了关于会话式推荐（SBR）的两项实证发现，并将其统一到单一模型DTAMLP中。首先，现有的时间感知及基于GNN的模型（如TiSASRec、SR-GNN）将所有点击时间间隔视为同等重要，尽管极短的停留时间往往反映出偶然点击，几乎不携带偏好信号——这一现象我们称为“零星噪声”。我们提出一种轻量、即插即用的权重融合模块，将模型的注意力权重与阈值截断的时间间隔权重相融合，该模块可几乎不改变模型架构地插入此类模型，且能持续提升准确率；我们将此视为本工作最直接可验证的贡献。其次，我们重新审视FMLP-Rec中一个未被充分解释的观察结果：对物品嵌入的可学习频域滤波器可提升准确率，并给出可能的解释：时域行为混合了多种纠缠的心理偏好，而频域视角可能让模型更自然地分离并降低此类偏好噪声的权重——这是一种解释性推测，而非已证实的机制。基于这两项见解，DTAMLP作为一种结合了权重融合与基于FFT的滤波的全MLP框架，在Diginetica和RetailRocket数据集上得到验证。尽管该系统级设计反映了2023年前后的领域状态，而非声称达到当前最优水平，但 ablation实验证实，这两种机制贡献了互补且非冗余的改进。
英文摘要
This paper reports two empirical findings on session-based recommendation (SBR), unified in a single model, DTAMLP. First, existing time-aware and GNN-based models (e.g., TiSASRec, SR-GNN) treat every click-time interval as equally informative, even though very short dwell times often reflect accidental clicks carrying little preference signal -- a phenomenon we call sporadic noise. We show that a lightweight, plug-and-play weight fusion module, blending a model's attention weight with a threshold-capped time-interval weight, can be inserted into such models with almost no architectural change and yields a consistent accuracy gain; we view this as the most directly verifiable contribution of this work. Second, we revisit an under-explained observation from FMLP-Rec, where a learnable frequency-domain filter on item embeddings improves accuracy, and offer a possible explanation: time-domain behavior mixes several entangled psychological preferences, and a frequency-domain view may let a model separate and down-weight such preference noise more naturally -- an interpretive conjecture rather than a proven mechanism. Building on both insights, DTAMLP, an all-MLP framework combining weight fusion and FFT-based filtering, is validated on Diginetica and RetailRocket. While this system-level design reflects the state of the field circa 2023 rather than a state-of-the-art claim, ablations confirm the two mechanisms contribute complementary, non-redundant improvements.
