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

2026-06-01

AI Summary

This work addresses the challenges in multi-behavior recommendation, where intra-behavior representation entanglement and inter-behavior reliability heterogeneity often introduce noise and deviate from the target intent. To this end, the authors propose SpectraMB, a novel model that introduces dynamic feature-level spectral filtering into multi-behavior recommendation for the first time. Operating in the frequency domain, it adaptively denoises representations without requiring predefined frequency bands. SpectraMB further incorporates a global context-aware attention mechanism that leverages the purified global representations as anchors to dynamically assess the reliability of each behavior, enabling reliability-aware fusion. Integrated with graph neural networks and a residual global backbone, SpectraMB significantly outperforms state-of-the-art methods on three real-world datasets, demonstrating enhanced recommendation accuracy and robustness to noise.

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.

Problem

• multi-behavior recommendation
• behavior-dependent noise
• representation entanglement
• reliability heterogeneity
• target-intent drift

Innovation

• Dynamic Spectral Denoising
• Global-Context Attention
• Multi-Behavior Recommendation
• Feature-Frequency Space
• Reliability-Aware Fusion