Dynamic Spectral Denoising with Global-Context Attention for Multi-Behavior Recommendation

• URL: http://arxiv.org/abs/2606.02417v1
• Date: Mon, 01 Jun 2026 15:58:13 GMT
• Title: Dynamic Spectral Denoising with Global-Context Attention for Multi-Behavior Recommendation
• Abstract summary: We argue that the key bottleneck is a representation-level failure caused by two coupled heterogeneities.<n>We propose Dynamic Spectral Denoising with Global-Context Attention for Multi-Behavior Recommendation (SpectraMB)<n>SpectraMB achieves the best results in most evaluation settings and exhibits improved robustness under noisy interactions.
• Score: 58.33732779555114
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Full spectrum Unlearnable Examples via Spectral Equalization [29.22443621401928]
  We propose Full-spectrum Unlearnable examples via Spectral Equalization (FUSE)<n>FUSE generates spectrum-agnostic perturbations by equalizing the contributions from different bands and enforcing cross-band consistency.<n>Experiments across multiple datasets, architectures, and spectral filtering demonstrate the strong protection achieved by FUSE.
  arXiv  Detail & Related papers  (2026-06-25T07:53:53Z)
• AnchorMoE: Interpretable Time Series Classification via Anchor-Routed MoE [51.994752158953084]
  AnchorMoE is an interpretable-by-construction classification framework.<n>It encodes multi-view representations of local patches and routes them to specialized experts.<n>Experiments on real-world and synthetic benchmarks demonstrate that AnchorMoE achieves highly competitive classification performance.
  arXiv  Detail & Related papers  (2026-06-02T13:30:54Z)
• Quality-Aware Robust Multi-View Clustering for Heterogeneous Observation Noise [12.720216418233795]
  We propose a novel framework termed Quality-Aware Robust Multi-View Clustering (QARMVC)<n>QARMVC employs an information bottleneck mechanism to extract intrinsic semantics for view reconstruction.<n>In experiments on five benchmark datasets, QARMVC consistently outperforms state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-02-26T03:16:44Z)
• Test-time Adaptive Hierarchical Co-enhanced Denoising Network for Reliable Multimodal Classification [55.56234913868664]
  We propose Test-time Adaptive Hierarchical Co-enhanced Denoising Network (TAHCD) for reliable learning on multimodal data.<n>The proposed method achieves superior classification performance, robustness, and generalization compared with state-of-the-art reliable multimodal learning approaches.
  arXiv  Detail & Related papers  (2026-01-12T03:14:12Z)
• Explainable Transformer-CNN Fusion for Noise-Robust Speech Emotion Recognition [2.0391237204597363]
  Speech Emotion Recognition systems often degrade in performance when exposed to unpredictable acoustic interference.<n>We propose a Hybrid Transformer-CNN framework that unifies the contextual modeling of Wav2Vec 2.0 with the spectral stability of 1D-Convolutional Neural Networks.
  arXiv  Detail & Related papers  (2025-12-20T10:05:58Z)
• Structured Spectral Reasoning for Frequency-Adaptive Multimodal Recommendation [13.886659472425393]
  Multimodal recommendation aims to integrate collaborative signals with heterogeneous content such as visual and textual information.<n>These issues are often exacerbated by naive fusion or shallow modeling strategies, leading to degraded generalization and poor robustness.<n>We propose a Structured Spectral Reasoning framework for frequency-aware multimodal recommendation.
  arXiv  Detail & Related papers  (2025-12-01T07:39:28Z)
• Evaluating ML Robustness in GNSS Interference Classification, Characterization & Localization [42.14439854721613]
  Jamming devices disrupt signals from the global navigation satellite system (GNSS)<n>This paper introduces an extensive dataset comprising snapshots obtained from a low-frequency antenna.<n>Our objective is to assess the resilience of machine learning (ML) models against environmental changes.
  arXiv  Detail & Related papers  (2024-09-23T15:20:33Z)
• Hybrid Convolutional and Attention Network for Hyperspectral Image Denoising [54.110544509099526]
  Hyperspectral image (HSI) denoising is critical for the effective analysis and interpretation of hyperspectral data. We propose a hybrid convolution and attention network (HCANet) to enhance HSI denoising. Experimental results on mainstream HSI datasets demonstrate the rationality and effectiveness of the proposed HCANet.
  arXiv  Detail & Related papers  (2024-03-15T07:18:43Z)
• Calibrating Undisciplined Over-Smoothing in Transformer for Weakly Supervised Semantic Segmentation [51.14107156747967]
  Weakly supervised semantic segmentation (WSSS) has attracted considerable attention because it requires fewer annotations than fully supervised approaches.<n>We propose an Adaptive Re-Activation Mechanism (AReAM) to control deep-level attention to undisciplined over-smoothing.<n>AReAM substantially improves segmentation performance compared with existing WSSS methods, reducing noise while sharpening focus on relevant semantic regions.
  arXiv  Detail & Related papers  (2023-05-04T19:11:33Z)
• Amplitude-Varying Perturbation for Balancing Privacy and Utility in Federated Learning [86.08285033925597]
  This paper presents a new DP perturbation mechanism with a time-varying noise amplitude to protect the privacy of federated learning. We derive an online refinement of the series to prevent FL from premature convergence resulting from excessive perturbation noise. The contribution of the new DP mechanism to the convergence and accuracy of privacy-preserving FL is corroborated, compared to the state-of-the-art Gaussian noise mechanism with a persistent noise amplitude.
  arXiv  Detail & Related papers  (2023-03-07T22:52:40Z)
• Improve Noise Tolerance of Robust Loss via Noise-Awareness [60.34670515595074]
  We propose a meta-learning method which is capable of adaptively learning a hyper parameter prediction function, called Noise-Aware-Robust-Loss-Adjuster (NARL-Adjuster for brevity) Four SOTA robust loss functions are attempted to be integrated with our algorithm, and comprehensive experiments substantiate the general availability and effectiveness of the proposed method in both its noise tolerance and performance.
  arXiv  Detail & Related papers  (2023-01-18T04:54:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.