# Multi-View Graph Convolutional Network for Multimedia Recommendation

arXiv: https://arxiv.org/abs/2308.03588 · HTML: https://mlsanity.com/paper/2308.03588/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2023.

## Built on by (most cited first, 30 of 32)

- [AlignRec: Aligning and Training in Multimodal Recommendations](https://mlsanity.com/paper/2403.12384/) (2024)
- [Multi-Modal Multi-Behavior Sequential Recommendation with Conditional Diffusion-Based Feature Denoising](https://mlsanity.com/paper/2508.05352/) (2025)
- [LD4MRec: Simplifying and Powering Diffusion Model for Multimedia Recommendation](https://mlsanity.com/paper/2309.15363/) (2023)
- [GUME: Graphs and User Modalities Enhancement for Long-Tail Multimodal Recommendation](https://mlsanity.com/paper/2407.12338/) (2024)
- [Multi-Modal Recommendation Unlearning for Legal, Licensing, and Modality Constraints](https://mlsanity.com/paper/2405.15328/) (2024)
- [Modality-Balanced Learning for Multimedia Recommendation](https://mlsanity.com/paper/2408.06360/) (2024)
- [LinkedOut: Linking World Knowledge Representation Out of Video LLM for Next-Generation Video Recommendation](https://mlsanity.com/paper/2512.16891/) (2025)
- [RSEA-MVGNN: Multi-View Graph Neural Network with Reliable Structural Enhancement and Aggregation](https://mlsanity.com/paper/2408.07331/) (2024)
- [Disentangling and Generating Modalities for Recommendation in Missing Modality Scenarios](https://mlsanity.com/paper/2504.16352/) (2025)
- [Dual-Phase Playtime-guided Recommendation: Interest Intensity Exploration and Multimodal Random Walks](https://mlsanity.com/paper/2508.14058/) (2025)
- [Training-free Adjustable Polynomial Graph Filtering for Ultra-fast Multimodal Recommendation](https://mlsanity.com/paper/2503.04406/) (2025)
- [Semantic Item Graph Enhancement for Multimodal Recommendation](https://mlsanity.com/paper/2508.06154/) (2025)
- [SiMilarity-Enhanced Homophily for Multi-View Heterophilous Graph Clustering](https://mlsanity.com/paper/2410.03596/) (2024)
- [Federated Vision-Language-Recommendation with Personalized Fusion](https://mlsanity.com/paper/2410.08478/) (2024)
- [Clustering-Oriented Generative Attribute Graph Imputation](https://mlsanity.com/paper/2507.19085/) (2025)
- [Bridging Domain Gaps between Pretrained Multimodal Models and Recommendations](https://mlsanity.com/paper/2502.15542/) (2025)
- [MDE: Modality Discrimination Enhancement for Multi-modal Recommendation](https://mlsanity.com/paper/2502.18481/) (2025)
- [EGRA:Toward Enhanced Behavior Graphs and Representation Alignment for Multimodal Recommendation](https://mlsanity.com/paper/2508.16170/) (2025)
- [A Scenario-Oriented Survey of Federated Recommender Systems: Techniques, Challenges, and Future Directions](https://mlsanity.com/paper/2508.19620/) (2025)
- [Multimodal Representation-disentangled Information Bottleneck for Multimodal Recommendation](https://mlsanity.com/paper/2509.20225/) (2025)
- [Structural and Disentangled Adaptation of Large Vision Language Models for Multimodal Recommendation](https://mlsanity.com/paper/2512.06883/) (2025)
- [Enhancing Imbalanced Node Classification via Curriculum-Guided Feature Learning and Three-Stage Attention Network](https://mlsanity.com/paper/2602.03808/) (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)
- [Modality-Guided Mixture of Structured Experts with Entropy-Triggered Routing for Multimodal Recommendation](https://mlsanity.com/paper/2602.20723/) (2026)
- [VLM4Rec: Multimodal Semantic Representation for Recommendation with Large Vision-Language Models](https://mlsanity.com/paper/2603.12625/) (2026)
- [TRU: Targeted Reverse Update for Efficient Multimodal Recommendation Unlearning](https://mlsanity.com/paper/2604.02183/) (2026)
- [User-Aware Conditional Generative Total Correlation Learning for Multi-Modal Recommendation](https://mlsanity.com/paper/2604.03014/) (2026)
- [Band Together: Untargeted Adversarial Training with Multimodal Coordination against Evasion-based Promotion Attacks](https://mlsanity.com/paper/2605.06238/) (2026)
- [Content Depth Matters in Short-Video Recommendation: Rethinking the Attention Economy](https://mlsanity.com/paper/2608.13990/) (2026)

## Abstract (arXiv)

Multimedia recommendation has received much attention in recent years. It models user preferences based on both behavior information and item multimodal information. Though current GCN-based methods achieve notable success, they suffer from two limitations: (1) Modality noise contamination to the item representations. Existing methods often mix modality features and behavior features in a single view (e.g., user-item view) for propagation, the noise in the modality features may be amplified and coupled with behavior features. In the end, it leads to poor feature discriminability; (2) Incomplete user preference modeling caused by equal treatment of modality features. Users often exhibit distinct modality preferences when purchasing different items. Equally fusing each modality feature ignores the relative importance among different modalities, leading to the suboptimal user preference modeling. To tackle the above issues, we propose a novel Multi-View Graph Convolutional Network for the multimedia recommendation. Specifically, to avoid modality noise contamination, the modality features are first purified with the aid of item behavior information. Then, the purified modality features of items and behavior features are enriched in separate views, including the user-item view and the item-item view. In this way, the distinguishability of features is enhanced. Meanwhile, a behavior-aware fuser is designed to comprehensively model user preferences by adaptively learning the relative importance of different modality features. Furthermore, we equip the fuser with a self-supervised auxiliary task. This task is expected to maximize the mutual information between the fused multimodal features and behavior features, so as to capture complementary and supplementary preference information simultaneously. Extensive experiments on three public datasets demonstrate the effectiveness of our methods.