Tags: Multimodal Recommendation Systems - Paper Library

Modality Alignment with Multi-scale Bilateral Attention for Multimodal Recommendation

Published:9/11/2025

Multimodal Recommendation SystemsFine-Grained Cross-Modal Association ModelingBidirectional Attention MechanismGlobal Distribution Consistency RegularizationDilated Refinement Attention Module

This study presents MambaRec, an innovative multimodal recommendation framework addressing finegrained crossmodal association modeling and global consistency issues through attentionguided learning. Its core contribution is the Dilated Refinement Attention Module, enhancing fu

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Curriculum Conditioned Diffusion for Multimodal Recommendation

Published:4/11/2025

Multimodal Recommendation SystemsDiffusion ModelsKnowledge-Aware Negative SamplingCurriculum Learning FrameworkMultimodal Aligning Module

The Curriculum Conditioned Diffusion framework (CCDRec) addresses data sparsity in multimodal recommendation by integrating diffusion models with negative sampling, enhancing personalization through the exploration of modality correlation. Its effectiveness and robustness are val

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UNGER: Generative Recommendation with A Unified Code via Semantic and Collaborative Integration

Published:10/28/2025

Generative Recommendation SystemsKnowledge Graph-based RecommendationPersonalized Recommendation SystemMultimodal Recommendation SystemsOnline Recommendation System Optimization

The paper introduces UNGER, a generative recommendation approach that integrates semantic and collaborative information into a unified code to reduce storage and inference costs. Utilizing a twophase framework for effective code construction, it demonstrates significant improvem

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Modality-Independent Graph Neural Networks with Global Transformers for Multimodal Recommendation

Published:12/19/2024

Multimodal Recommendation SystemsGraph Neural NetworksModality-Independent Receptive FieldsGlobal TransformerUser-Item Graph Modeling

This study presents modalityindependent GNNs to enhance multimodal recommendation performance by utilizing separate GNNs for different modalities. A samplingbased global transformer effectively integrates global information, addressing limitations of existing methods, with supe

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Multimodal fusion framework based on knowledge graph for personalized recommendation

Published:1/1/2025

Knowledge Graph-based RecommendationMultimodal Recommendation SystemsMultimodal Fusion FrameworkPersonalized Recommendation

This work proposes MultiKG4Rec, a multimodal fusion framework leveraging finegrained modal interactions in knowledge graphs to enhance personalized recommendations, demonstrating superior efficiency on realworld datasets.

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Knowledge graph-based personalized multimodal recommendation fusion framework

Published:1/1/2025

Knowledge Graph-based RecommendationMultimodal Recommendation SystemsCross-Modal Multi-Head Cross-AttentionGraph Attention NetworksPretrained Vision-Text Models

We propose CrossGMMIDUKGLR, a knowledge graphbased multimodal recommendation framework using pretrained visualtext alignment, multihead crossattention, and graph attention networks to enhance feature fusion and capture higherorder dependencies for improved personalization.

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DiffCL: A Diffusion-Based Contrastive Learning Framework with Semantic Alignment for Multimodal Recommendations

Published:1/2/2025

Multimodal Recommendation SystemsDiffusion ModelsContrastive Learning FrameworkSemantic AlignmentGraph-Based Feature Enhancement

DiffCL is a diffusionbased contrastive learning framework for multimodal recommendation that reduces noise via diffusiongenerated views, aligns crossmodal semantics with stable ID embeddings, and alleviates data sparsity using an itemitem graph, enhancing recommendation accur

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Papers