Beyond Noisy Signals: Dual-Level Denoising for Multi-modal Sequential Recommendation

• URL: http://arxiv.org/abs/2607.18786v1
• Date: Tue, 21 Jul 2026 07:06:06 GMT
• Title: Beyond Noisy Signals: Dual-Level Denoising for Multi-modal Sequential Recommendation
• Abstract summary: We propose textbfDual-level textbfDenoising textbfMulti-modal textbfSequential textbfRecommendation framework.
• Score: 6.902443075506667
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-modal Sequential Recommendation (SR) incorporates rich side information (e.g., textual and visual features) to enhance dynamic user preference modeling. However, existing frameworks inevitably suffer from a \textbf{Dual-Noise Dilemma}: (1) \textit{Feature-level redundancy} stemming from the semantic gap between generic pre-trained representations and fine-grained recommendation intent; and (2) \textit{Sequence-level stochasticity} induced by spurious interactions such as accidental clicks. To break this bottleneck, we propose \textbf{DDMSR}, a novel \textbf{D}ual-level \textbf{D}enoising \textbf{M}ulti-modal \textbf{S}equential \textbf{R}ecommendation framework that systematically purifies signals from both feature-topological and sequence-frequency perspectives. Specifically, we first design a graph-based feature denoising module that leverages Laplacian smoothing on item semantic graphs as a structural low-pass filter, effectively suppressing high-frequency semantic noise while preserving salient features. For sequence purification, we introduce a frequency-domain sequence denoising module, utilizing the Fast Fourier Transform and a learnable frequency filter to adaptively modulate the interaction spectrum and attenuate anomalous signals. Furthermore, a multi-modal contrastive alignment objective is incorporated to bridge the heterogeneity gap and enforce cross-modal semantic consistency. Extensive experiments on four public benchmark datasets demonstrate that DDMSR consistently outperforms state-of-the-art baselines, providing a highly robust and efficient solution for multi-modal sequential recommendation. The source code is available at: ~\href{https://github.com/jluo00/DDMSR}{\textcolor{blue}{https://github.com/jluo00/DDMSR}}.

Related papers

• Dynamic Spectral Denoising with Global-Context Attention for Multi-Behavior Recommendation [58.33732779555114]
  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.
  arXiv  Detail & Related papers  (2026-06-01T15:58:13Z)
• Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning [8.080589414732982]
  Conventional Graph Contrastive Learning (GCL) on Text-Attributed Graphs (TAGs) relies on blind augmentations, inadvertently entangling task-relevant signals with noise.<n>We propose SDM-SCR, a robust framework anchored in Approximate Orthogonal Decomposition.
  arXiv  Detail & Related papers  (2026-04-16T07:57:11Z)
• Subtractive Modulative Network with Learnable Periodic Activations [59.89799070130572]
  We propose a novel, parameter-efficient Implicit Neural Representation architecture inspired by classical subtractive synthesis.<n>Our SMN achieves a PSNR of $40+$ dB on two image datasets, comparing favorably against state-of-the-art methods in terms of both reconstruction accuracy and parameter efficiency.
  arXiv  Detail & Related papers  (2026-02-18T10:20:50Z)
• FITMM: Adaptive Frequency-Aware Multimodal Recommendation via Information-Theoretic Representation Learning [14.873780184982003]
  We propose a Frequency-aware Information-Theoretic framework for multimodal recommendation.<n> FITMM constructs graph-enhanced item representations, performs modality-wise spectral decomposition, and forms lightweight within-band multimodal components.<n>Experiments on three real-world datasets demonstrate that FITMM consistently and significantly outperforms advanced baselines.
  arXiv  Detail & Related papers  (2026-01-30T03:16:54Z)
• 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)
• DynaPURLS: Dynamic Refinement of Part-aware Representations for Skeleton-based Zero-Shot Action Recognition [51.80782323686666]
  We introduce textbfDynaPURLS, a unified framework that establishes robust, multi-scale visual-semantic correspondences.<n>Our framework leverages a large language model to generate hierarchical textual descriptions that encompass both global movements and local body-part dynamics.<n>Experiments on three large-scale benchmark datasets, including NTU RGB+D 60/120 and PKU-MMD, demonstrate that DynaPURLS significantly outperforms prior art.
  arXiv  Detail & Related papers  (2025-12-12T10:39:10Z)
• From Points to Clouds: Learning Robust Semantic Distributions for Multi-modal Prompts [11.693848445032259]
  Multimodal Prompt Learning (MPL) has emerged as a pivotal technique for adapting large-scale Visual Language Models (VLMs)<n>We introduce Points-to-Clouds (P2C), a novel framework inspired by diffusion models that reframe prompt learning as a dynamic denoising task.<n>P2C consistently outperforms strong baselines in experiments across 11 datasets.
  arXiv  Detail & Related papers  (2025-11-28T06:03:35Z)
• FindRec: Stein-Guided Entropic Flow for Multi-Modal Sequential Recommendation [57.577843653775]
  We propose textbfFindRec (textbfFlexible unified textbfinformation textbfdisentanglement for multi-modal sequential textbfRecommendation)<n>A Stein kernel-based Integrated Information Coordination Module (IICM) theoretically guarantees distribution consistency between multimodal features and ID streams.<n>A cross-modal expert routing mechanism that adaptively filters and combines multimodal features based on their contextual relevance.
  arXiv  Detail & Related papers  (2025-07-07T04:09:45Z)
• Graph with Sequence: Broad-Range Semantic Modeling for Fake News Detection [18.993270952535465]
  BREAK is a broad-range semantics model for fake news detection.<n>It leverages a fully connected graph to capture comprehensive semantics.<n>It employs dual denoising modules to minimize both structural and feature noise.
  arXiv  Detail & Related papers  (2024-12-07T14:35:46Z)
• The Silent Assistant: NoiseQuery as Implicit Guidance for Goal-Driven Image Generation [31.599902235859687]
  We propose to leverage an aligned Gaussian noise as implicit guidance to complement explicit user-defined inputs, such as text prompts.<n>NoiseQuery enables fine-grained control and yields significant performance boosts over high-level semantics and over low-level visual attributes.
  arXiv  Detail & Related papers  (2024-12-06T14:59:00Z)
• High-Fidelity Speech Synthesis with Minimal Supervision: All Using Diffusion Models [56.00939852727501]
  Minimally-supervised speech synthesis decouples TTS by combining two types of discrete speech representations. Non-autoregressive framework enhances controllability, and duration diffusion model enables diversified prosodic expression.
  arXiv  Detail & Related papers  (2023-09-27T09:27:03Z)
• Conditional Denoising Diffusion for Sequential Recommendation [62.127862728308045]
  Two prominent generative models, Generative Adversarial Networks (GANs) and Variational AutoEncoders (VAEs) GANs suffer from unstable optimization, while VAEs are prone to posterior collapse and over-smoothed generations. We present a conditional denoising diffusion model, which includes a sequence encoder, a cross-attentive denoising decoder, and a step-wise diffuser.
  arXiv  Detail & Related papers  (2023-04-22T15:32:59Z)

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.