One Graph, Multiple Gains: Single High-Quality Item-Item Graph for Multimodal Recommendation

• URL: http://arxiv.org/abs/2607.24607v1
• Date: Mon, 27 Jul 2026 16:09:28 GMT
• Title: One Graph, Multiple Gains: Single High-Quality Item-Item Graph for Multimodal Recommendation
• Abstract summary: IIMRec is a framework that constructs a single high-quality item-item graph during preprocessing.<n>The graph is built by fusing semantic and co-occurrence signals, then refined via Neighborhood Consistency Edge Reweighting.<n>IIMRec consistently outperforms state-of-the-art baselines while running faster and consuming less GPU memory.
• Score: 12.896323268160744
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal recommendation leverages item multimodal features alongside collaborative signals to capture user preferences. While item-item graphs have become a key building block in advanced models, existing methods typically construct them with noisy similarity edges and limit their role to a single function of item-item representation propagation, leaving substantial potential untapped. In this paper, we propose IIMRec, a framework that constructs a single high-quality item-item graph during preprocessing and systematically reuses it across three stages of the recommendation pipeline: representation enhancement, interaction graph enhancement, and optimization enhancement. The graph is built by fusing semantic and co-occurrence signals, then refined via Neighborhood Consistency Edge Reweighting (NCER), which applies the triadic closure principle to amplify structurally reliable edges and suppress spurious ones. Once constructed, the graph is leveraged in three complementary ways: (1) Item-item propagation with a Residual II Gate (RIG) that adaptively controls per-item absorption of semantic neighborhood signals for representation enhancement; (2) A content-guided UI graph expansion that introduces virtual user-item edges through high-confidence semantic neighbors for interaction graph enhancement; (3) II-Neighbor BPR Augmentation (INA) that treats top neighbors of positive items as discounted soft positives for optimization enhancement. We provide theoretical analysis showing that NCER reduces the spectral noise-to-signal ratio, RIG converges to a non-degenerate gating regime, and INA yields a tighter generalization bound. Extensive experiments on four datasets demonstrate that IIMRec consistently outperforms state-of-the-art baselines while running faster and consuming less GPU memory, with particularly strong gains under cold-start and sparse-interaction conditions.

Related papers

• Beyond the Aggregation Dilemma: Prior-Retaining Decoupled Learning for Multimodal Graphs [69.56407708794845]
  MultimodalAttributed Graph Learning (MAGL) integrates node attributes with structural topology via graph aggregation.<n>As pretrained encoders evolve into Large Foundation Models (LFMs), the landscape of MAGL fundamentally shifts.<n>Under high-confidence LFM priors, mandatory aggregation introduces topological intrinsic noise that overwhelms discriminative signals.<n>We propose SUPRA (Shared-Unique Prior-Retaining Architecture), a decoupled dual-pathway paradigm.<n> SUPRA processes modality-specific features through topology-agnostics while capturing structural synergy via a lightweight shared GNN, with auxiliary deep supervision counteracting gradient starvation.
  arXiv  Detail & Related papers  (2026-05-23T17:42:25Z)
• Beyond the Flat Sequence: Hierarchical and Preference-Aware Generative Recommendations [35.58864660038236]
  We propose a novel framework named HPGR (Hierarchical and Preference-aware Generative Recommender)<n>First, a structure-aware pre-training stage employs a session-based Masked Item Modeling objective to learn a hierarchically-informed and semantically rich item representation space.<n>Second, a preference-aware fine-tuning stage leverages these powerful representations to implement a Preference-Guided Sparse Attention mechanism.
  arXiv  Detail & Related papers  (2026-03-01T08:15:34Z)
• CSMCIR: CoT-Enhanced Symmetric Alignment with Memory Bank for Composed Image Retrieval [54.15776146365823]
  Composed Image Retrieval (CIR) enables users to search for target images using both a reference image and manipulation text.<n>We propose CSMCIR, a unified representation framework that achieves efficient query-target alignment through three synergistic components.
  arXiv  Detail & Related papers  (2026-01-07T09:21:38Z)
• IGDMRec: Behavior Conditioned Item Graph Diffusion for Multimodal Recommendation [21.87097387902408]
  Multimodal recommender systems (MRSs) are critical for various online platforms, offering users more accurate personalized recommendations by incorporating multimodal information.<n>We propose Item Graph Diffusion for Multimodal Recommendation (IGDMRec), a novel method that leverages a diffusion model with classifier-free guidance to denoise the semantic item graph.<n>Extensive experiments on four real-world datasets demonstrate the superiority of IGDMRec over competitive baselines.
  arXiv  Detail & Related papers  (2025-12-23T02:13:01Z)
• Decoupling and Damping: Structurally-Regularized Gradient Matching for Multimodal Graph Condensation [3.2987327415317895]
  We propose Structurally-Regularized Gradient Matching (SR-GM), a novel condensation framework tailored for multimodal graphs.<n> SR-GM significantly improves accuracy and accelerates convergence compared to baseline methods.<n>This research provides a scalable methodology for multimodal graph-based learning in resource-constrained environments.
  arXiv  Detail & Related papers  (2025-11-25T11:50:34Z)
• Semantic Item Graph Enhancement for Multimodal Recommendation [49.66272783945571]
  Multimodal recommendation systems have attracted increasing attention for their improved performance by leveraging items' multimodal information.<n>Prior methods often build modality-specific item-item semantic graphs from raw modality features.<n>These semantic graphs suffer from semantic deficiencies, including insufficient modeling of collaborative signals among items.
  arXiv  Detail & Related papers  (2025-08-08T09:20:50Z)
• Contrastive Matrix Completion with Denoising and Augmented Graph Views for Robust Recommendation [1.0128808054306186]
  Matrix completion is a widely adopted framework in recommender systems.<n>We propose a novel method called Matrix Completion using Contrastive Learning (MCCL)<n>Our approach not only improves the numerical accuracy of the predicted scores--but also produces superior rankings with improvements of up to 36% in ranking metrics.
  arXiv  Detail & Related papers  (2025-06-12T12:47:35Z)
• Analysis of Anonymous User Interaction Relationships and Prediction of Advertising Feedback Based on Graph Neural Network [5.250286096386298]
  We propose Decoupled Temporal-Hierarchical Graph Neural Network (DTH-GNN), which achieves three main contributions.<n> Firstly, we introduce temporal edge decomposition, which divides each interaction into three types of channels: short-term burst, diurnal cycle and long-range memory, and conducts feature extraction using the convolution kernel of parallel dilated residuals.<n>Thirdly, the contrast of feedback perception is formulated, the consistency of various time slices is maximized, the entropy of control exposure information with dual-view target is maximized, and the global prototype of dual-momentum queue distillation is presented.
  arXiv  Detail & Related papers  (2025-06-11T04:40:24Z)
• MCGS: Multiview Consistency Enhancement for Sparse-View 3D Gaussian Radiance Fields [100.90743697473232]
  Radiance fields represented by 3D Gaussians excel at synthesizing novel views, offering both high training efficiency and fast rendering.<n>Existing methods often incorporate depth priors from dense estimation networks but overlook the inherent multi-view consistency in input images.<n>We propose a view synthesis framework based on 3D Gaussian Splatting, enabling scene reconstruction from sparse views.
  arXiv  Detail & Related papers  (2024-10-15T08:39:05Z)
• S^2Former-OR: Single-Stage Bi-Modal Transformer for Scene Graph Generation in OR [50.435592120607815]
  Scene graph generation (SGG) of surgical procedures is crucial in enhancing holistically cognitive intelligence in the operating room (OR) Previous works have primarily relied on multi-stage learning, where the generated semantic scene graphs depend on intermediate processes with pose estimation and object detection. In this study, we introduce a novel single-stage bi-modal transformer framework for SGG in the OR, termed S2Former-OR.
  arXiv  Detail & Related papers  (2024-02-22T11:40:49Z)
• Graph Signal Sampling for Inductive One-Bit Matrix Completion: a Closed-form Solution [112.3443939502313]
  We propose a unified graph signal sampling framework which enjoys the benefits of graph signal analysis and processing. The key idea is to transform each user's ratings on the items to a function (signal) on the vertices of an item-item graph. For the online setting, we develop a Bayesian extension, i.e., BGS-IMC which considers continuous random Gaussian noise in the graph Fourier domain.
  arXiv  Detail & Related papers  (2023-02-08T08:17:43Z)
• Spatial-spectral Hyperspectral Image Classification via Multiple Random Anchor Graphs Ensemble Learning [88.60285937702304]
  This paper proposes a novel spatial-spectral HSI classification method via multiple random anchor graphs ensemble learning (RAGE) Firstly, the local binary pattern is adopted to extract the more descriptive features on each selected band, which preserves local structures and subtle changes of a region. Secondly, the adaptive neighbors assignment is introduced in the construction of anchor graph, to reduce the computational complexity.
  arXiv  Detail & Related papers  (2021-03-25T09:31:41Z)

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.