MURAL is a multimodal recommendation framework that replaces static similarity graphs with a dynamic topology discovery process. It uses an Adaptive Edge Learner to find latent item-item correlations efficiently and an Uncertainty-Aware Fusion module to down‑weight noisy modality signals based on aleatoric uncertainty. The model also incorporates a contrastive teacher‑student alignment to stabilize training and has been shown to outperform state‑of‑the‑art baselines on large‑scale TikTok and Amazon datasets, providing both higher accuracy and interpretability.
arXiv:2603. 27723v2 Announce Type: replace Abstract: Multimodal-attributed graphs (MAGs) are a fundamental data structure for multimodal graph learning (MGL), enabling both graph-centric and modality-centric tasks.
arXiv:2607. 15687v1 Announce Type: new Abstract: Multimodal-attributed graphs (MAGs), whose nodes carry modalities such as images and text alongside topological structure, now pervade applications including social platforms, e-commerce, and biomedical networks, offering richer semantic signals than single-modality graphs.
arXiv:2606. 12867v2 Announce Type: replace Abstract: Multimodal-attributed graphs (MAGs) couple graph topology with node semantics from text, images, and other modalities.
arXiv:2606. 14172v1 Announce Type: new Abstract: Multimodal Attributed Graphs (MAGs) model real-world entities by coupling graph topology with heterogeneous attributes such as text and images.
arXiv:2603. 17450v2 Announce Type: replace-cross Abstract: Sequential Recommendation (SR) in multimodal settings typically relies on small frozen pretrained encoders, which limits semantic capacity and prevents Collaborative Filtering (CF) signals from being fully integrated into item representations.
arXiv:2609.06668v1 Announce Type: new Abstract: Multimodal graphs couple node attributes in different modalities, such as text and images, with relational structure, enabling topological structure an...
arXiv:2608. 00623v1 Announce Type: new Abstract: Multimodal-attributed graphs (MAGs), where nodes carry heterogeneous semantic content across multiple modalities while edges encode relational dependencies, have been widely adopted across diverse domains.
arXiv:2610.19578v1 Announce Type: cross Abstract: Graph Collaborative Filtering models have achieved remarkable success in recommendation systems. However, they inherently suffer from a catastrophic...
The paper introduces OrthoRec, a multimodal recommender system that challenges the common "modality harmony" assumption by addressing conflicts between multimodal features and collaborative patterns. It employs Collaborative‑Guided Orthogonal Purification (CGOP) to separate useful multimodal signals from noisy orthogonal components, and a Topology‑Aware Routing Mixture‑of‑Experts (TAR‑MoE) to adaptively integrate purified modalities based on collaborative topology. Experiments on Amazon datasets demonstrate that OrthoRec outperforms recent baselines and remains robust to modality noise and item sparsity.
GOMA (Graph-Optimized Multimodal Alignment) introduces a dual-embedding approach for multimodal retrieval, separating content embeddings supervised for paired identity from semantic embeddings trained with cross-modal pairs and observed relationships. The method fuses these embeddings, applies semantic agreement to weight graph edges, and uses restart graph propagation to reinforce the initial signal, enabling both single-modality and dual-attribute retrieval. Across six datasets and four tasks, GOMA outperforms 14 external methods on 14 primary metrics, with controlled experiments highlighting the impact of separate supervision, graph regularization, and semantic-guided propagation.
Graph Collaborative Filtering models have achieved remarkable success in recommendation systems. However, they inherently suffer from a catastrophic representation collapse in strict cold-start scenar...
arXiv:2606. 20382v1 Announce Type: new Abstract: MultiModal Federated Graph Learning (MM-FGL) offers a natural collaborative training paradigm, but its practical deployment is challenged by two granularities of modality imbalance.