Multi-behavior recommendation improves target-behavior prediction by exploiting heterogeneous auxiliary feedback (e.g., view, collect, and cart), yet its robustness is undermined by behavior-dependent noise and inconsistency. We argue that the key bottleneck is a representation-level failure caused by two coupled heterogeneities. First, intra-behavior representation entanglement arises when multi-hop propagation blends incidental signals with true preferences in the embedding space, making coarse spatial denoising unable to suppress noise without sacrificing informative niche signals. Second, inter-behavior reliability heterogeneity complicates cross-behavior fusion because the predictive value of auxiliary behaviors varies across users and contexts. Without reliability calibration, frequent yet unreliable signals may dominate aggregation and cause target-intent drift. To address this bottleneck, we propose Dynamic Spectral Denoising with Global-Context Attention for Multi-Behavior Recommendation (SpectraMB), a target-oriented model that performs representation purification before reliability-aware fusion. SpectraMB introduces Dynamic Feature-Level Spectral Filtering, which re-parameterizes embeddings along the feature dimension into a feature-frequency space and learns view-adaptive spectral modulation under target supervision, enabling component-wise purification without hand-crafted frequency assumptions. It further proposes Global-Context Attention Fusion, which uses a purified global representation as a context anchor to assess view compatibility and perform reliability-aware aggregation, while a residual global backbone preserves collaborative structure. Extensive experiments on three real-world datasets show that SpectraMB achieves the best results in most evaluation settings and exhibits improved robustness under noisy interactions.

翻译：多行为推荐通过利用异质辅助反馈（如浏览、收藏、加购）来提升目标行为预测性能，但其鲁棒性受到行为相关噪声与不一致性的损害。我们认为核心瓶颈在于两种耦合异质性导致的表征层面失效。首先，行为内表征纠缠源于多跳传播将偶然信号与真实偏好混杂在嵌入空间中，使得粗粒度的空间去噪无法在不牺牲信息性微弱信号的前提下抑制噪声。其次，行为间可靠性异质性使跨行为融合复杂化——辅助行为的预测价值随用户和上下文动态变化。缺乏可靠性校准将导致频繁但不可靠的信号主导聚合，进而引发目标意图漂移。针对该瓶颈，我们提出面向多行为推荐的动态谱去噪与全局上下文注意力模型（SpectraMB），一种在可靠性感知融合前执行表征净化的目标导向模型。SpectraMB引入动态特征级谱滤波，沿特征维度将嵌入重新参数化至特征-频率空间，并在目标监督下学习视图自适应谱调制，无需人工预设频率假设即可实现成分级净化。进一步提出全局上下文注意力融合机制，以净化后的全局表征作为上下文锚点评估视图兼容性并执行可靠性感知聚合，同时通过残差全局骨干网络保留协同结构。在三个真实数据集上的大量实验表明，SpectraMB在多数评估设置中取得最佳结果，并在含噪交互场景下展现出更优的鲁棒性。