Sequential recommendation aims to model users' evolving interests from noisy and non-stationary interaction streams, where long-term preferences, short-term intents, and localized behavioral fluctuations may coexist across temporal scales. Existing frequency-domain methods mainly rely on either global spectral operations or filter-based wavelet processing. However, global spectral operations tend to entangle local transients with long-range dependencies, while filter-based wavelet pipelines may suffer from temporal misalignment and boundary artifacts during multi-scale decomposition and reconstruction. Moreover, collaborative signals from the user-item interaction graph are often injected through scale-inconsistent auxiliary modules, limiting the benefit of jointly modeling temporal dynamics and structural dependencies. To address these issues, we propose Wavelet Packet Guided Graph Enhanced Sequential Recommendation (WPGRec), a unified time-frequency and graph-enhanced framework that aligns multi-resolution temporal modeling with graph propagation at matching scales. WPGRec first applies a full-tree undecimated stationary wavelet packet transform to generate equal-length, shift-invariant subband sequences. It then performs subband-wise interaction-graph propagation to inject high-order collaborative information while preserving temporal alignment across resolutions. Finally, an energy- and spectral-flatness-aware gated fusion module adaptively aggregates informative subbands and suppresses noise-like components. Extensive experiments on four public benchmarks show that WPGRec consistently outperforms sequential and graph-based baselines, with particularly clear gains on sparse and behaviorally complex datasets, highlighting the effectiveness of band-consistent structure injection and adaptive subband fusion for sequential recommendation.

翻译：序列推荐旨在从噪声和非平稳的交互流中建模用户不断变化的兴趣，其中长期偏好、短期意图和局部行为波动可能跨时间尺度共存。现有频域方法主要依赖于全局频谱操作或基于滤波器的小波处理。然而，全局频谱操作容易将局部瞬态与长程依赖纠缠在一起，而基于滤波器的小波流程在多尺度分解与重构过程中可能遭受时间错位和边界伪影的影响。此外，来自用户-物品交互图的协同信号通常通过尺度不一致的辅助模块注入，限制了联合建模时间动态与结构依赖的收益。为解决这些问题，我们提出小波包引导的图增强序列推荐（WPGRec），这是一个统一的时频与图增强框架，在匹配尺度上对齐多分辨率时间建模与图传播。WPGRec首先应用全树非抽取平稳小波包变换生成等长、平移不变的子带序列，然后执行子带级交互图传播，注入高阶协同信息同时保持跨分辨率的时间对齐。最后，一种考虑能量与频谱平坦度的门控融合模块自适应聚合信息丰富的子带，并抑制噪声类成分。在四个公开基准上的大量实验表明，WPGRec一致优于基于序列和图的基线方法，在稀疏和行为复杂的数据集上效果尤为显著，凸显了频带一致的结构注入与自适应子带融合对序列推荐的有效性。