# UPGCN: User Perception-Guided Graph Convolutional Network for Multimodal Recommendation
## 2 Related Work
### Graph-Based Multimodal Models  
Graph neural networks (GNNs) have proven effective in learning representations for graph data across various domains [32; 33; 34]. The core concept of GNNs is to update the representation of the current node by aggregating the representations of its neighboring nodes during the propagation process. Recently, more and more researchers have introduced GNNs into recommendation systems. In collaborative filtering recommendations, there are only two entities: users and items. The relationships between them can be representedby an interaction graph, allowing for the application of GNNs in this recommendation context. Specifically, graph-based approaches represent users and items by transforming user interaction histories into an interaction graph between users and items.  
In order to better utilize multimodal information and improve recommendation performance, multimodal recommendation methods have emerged. MMGCN [13] constructs user-item interaction graphs across multiple modalities, then employs GNN to combine information from neighboring nodes, fusing the user and item representations across multiple modalities, thereby enhancing the understanding of user preferences. Based on MMGCN, DualGNN [23] obtains user representations under different modalities through GNN and then establishes a user co-occurrence graph to capture users' modal preferences across different modalities. By identifying false-positive edges and noisy edges, GRCN [14] has created a graph refining layer that further refines the interaction graph between users and items, therefore, preventing the propagation and aggregation of noise in the GNN. However, the aforementioned method overlooks the relationships between items. To more accurately obtain the relationship between items, LATTICE [24] establishes the item-item graphs for different modalities and then integrates them to obtain a latent item-item graph. Additionally, during the backpropagation process to optimize parameters, the item-item relationship graph will be updated. MICRO [35] is an extension of LATTICE, that proposes a contrastive modality fusion method to solve the inconsistency problem in multimodal information fusion by combining contrastive learning and latent structure mining. FREEDOM [25] finds that learning the potential structure was inefficient, so it freezes the updates of the item-item graphs across various modalities, allowing for better item semantic representation and improving the algorithm's efficiency. BM3 [36] utilizes self-supervised learning methods to solve the negative sample sampling problem and the complexity problem of large-scale graph computation in multimodal recommendation. The model generates a contrastive view by using a simple dropout data augmentation technique, replacing the existing graph structure enhancement methods and thus reducing the computational and memory overhead. Following this line of research, we utilize graph convolutional networks and multimodal features to initially obtain the users' modality preferences and then adaptively learn user representations based on these preferences to achieve more accurate user preferences and improve recommendation performance.