# LGMRec: Local and Global Graph Learning for Multimodal Recommendation

arXiv: https://arxiv.org/abs/2312.16400 · HTML: https://mlsanity.com/paper/2312.16400/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Dec 2023.

## Built on by (most cited first, 30 of 38)

- [OpenMAG: A Comprehensive Benchmark for Multimodal-Attributed Graph](https://mlsanity.com/paper/2602.05576/) (2026)
- [Disentangling and Generating Modalities for Recommendation in Missing Modality Scenarios](https://mlsanity.com/paper/2504.16352/) (2025)
- [TMTE: Effective Multimodal Graph Learning with Task-aware Modality and Topology Co-evolution](https://mlsanity.com/paper/2603.27723/) (2026)
- [RoleMAG: Learning Neighbor Roles in Multimodal Graphs](https://mlsanity.com/paper/2604.12271/) (2026)
- [Training-free Adjustable Polynomial Graph Filtering for Ultra-fast Multimodal Recommendation](https://mlsanity.com/paper/2503.04406/) (2025)
- [MMHCL: Multi-Modal Hypergraph Contrastive Learning for Recommendation](https://mlsanity.com/paper/2504.16576/) (2025)
- [Efficient Multimodal Streaming Recommendation via Expandable Side Mixture-of-Experts](https://mlsanity.com/paper/2508.05993/) (2025)
- [Semantic Item Graph Enhancement for Multimodal Recommendation](https://mlsanity.com/paper/2508.06154/) (2025)
- [CAMPA: Efficient and Aligned Multimodal Graph Learning via Decoupled Propagation and Aggregation](https://mlsanity.com/paper/2605.11468/) (2026)
- [ELMM: Efficient Lightweight Multimodal Large Language Models for Multimodal Knowledge Graph Completion](https://mlsanity.com/paper/2510.16753/) (2025)
- [Modality as Heterogeneity: Node Splitting and Graph Rewiring for Multimodal Graph Learning](https://mlsanity.com/paper/2602.00067/) (2026)
- [MoToRec: Sparse-Regularized Multimodal Tokenization for Cold-Start Recommendation](https://mlsanity.com/paper/2602.11062/) (2026)
- [Context-aware Modality-Topology Co-Alignment for Multimodal Attributed Graphs](https://mlsanity.com/paper/2606.14172/) (2026)
- [Towards Effective Federated Multimodal Graph Learning via Navigating Multifaceted Heterogeneity](https://mlsanity.com/paper/2608.00623/) (2026)
- [MDE: Modality Discrimination Enhancement for Multi-modal Recommendation](https://mlsanity.com/paper/2502.18481/) (2025)
- [Leveraging Multimodal Data and Side Users for Diffusion Cross-Domain Recommendation](https://mlsanity.com/paper/2507.04000/) (2025)
- [EGRA:Toward Enhanced Behavior Graphs and Representation Alignment for Multimodal Recommendation](https://mlsanity.com/paper/2508.16170/) (2025)
- [Multimodal Representation-disentangled Information Bottleneck for Multimodal Recommendation](https://mlsanity.com/paper/2509.20225/) (2025)
- [Structurally Refined Graph Transformer for Multimodal Recommendation](https://mlsanity.com/paper/2511.00584/) (2025)
- [Cross-Modal Attention Network with Dual Graph Learning in Multimodal Recommendation](https://mlsanity.com/paper/2601.11151/) (2026)
- [Multimodal Enhancement of Sequential Recommendation](https://mlsanity.com/paper/2602.07207/) (2026)
- [Sequences as Nodes for Contrastive Multimodal Graph Recommendation](https://mlsanity.com/paper/2602.07208/) (2026)
- [E-MMKGR: A Unified Multimodal Knowledge Graph Framework for E-commerce Applications](https://mlsanity.com/paper/2602.20877/) (2026)
- [CAMMSR: Category-Guided Attentive Mixture of Experts for Multimodal Sequential Recommendation](https://mlsanity.com/paper/2603.04320/) (2026)
- [Anchored Alignment: Preventing Positional Collapse in Multimodal Recommender Systems](https://mlsanity.com/paper/2603.12726/) (2026)
- [User-Aware Conditional Generative Total Correlation Learning for Multi-Modal Recommendation](https://mlsanity.com/paper/2604.03014/) (2026)
- [Joint Behavior-guided and Modality-coherence Conditional Graph Diffusion Denoising for Multi Modal Recommendation](https://mlsanity.com/paper/2604.03654/) (2026)
- [Revisiting Content-Based Music Recommendation: Efficient Feature Aggregation from Large-Scale Music Models](https://mlsanity.com/paper/2604.20847/) (2026)
- [Band Together: Untargeted Adversarial Training with Multimodal Coordination against Evasion-based Promotion Attacks](https://mlsanity.com/paper/2605.06238/) (2026)
- [GOMA: Toward Structure-Driven Multimodal Alignment from a Graph Signal Smoothing Perspective](https://mlsanity.com/paper/2605.15723/) (2026)

## Abstract (arXiv)

The multimodal recommendation has gradually become the infrastructure of online media platforms, enabling them to provide personalized service to users through a joint modeling of user historical behaviors (e.g., purchases, clicks) and item various modalities (e.g., visual and textual). The majority of existing studies typically focus on utilizing modal features or modal-related graph structure to learn user local interests. Nevertheless, these approaches encounter two limitations: (1) Shared updates of user ID embeddings result in the consequential coupling between collaboration and multimodal signals; (2) Lack of exploration into robust global user interests to alleviate the sparse interaction problems faced by local interest modeling. To address these issues, we propose a novel Local and Global Graph Learning-guided Multimodal Recommender (LGMRec), which jointly models local and global user interests. Specifically, we present a local graph embedding module to independently learn collaborative-related and modality-related embeddings of users and items with local topological relations. Moreover, a global hypergraph embedding module is designed to capture global user and item embeddings by modeling insightful global dependency relations. The global embeddings acquired within the hypergraph embedding space can then be combined with two decoupled local embeddings to improve the accuracy and robustness of recommendations. Extensive experiments conducted on three benchmark datasets demonstrate the superiority of our LGMRec over various state-of-the-art recommendation baselines, showcasing its effectiveness in modeling both local and global user interests.