# UPGCN: User Perception-Guided Graph Convolutional Network for Multimodal Recommendation
## 1 Introduction
DARGON [16] learns dual representations of users and items by constructing a multimodal recommendation homogeneous graph, thus enhancing the binary relationship. MARIO [17] predicts users' preferences by considering the individual impact of each modality on each interaction, while obtaining item embeddings that preserve the inherent modal-specific properties. MKGCN [18] builds three aggregators, where the multimodal aggregator aggregates the multimodal features of the item into a multimodal knowledge graph, the user aggregator and item aggregator use graph convolutional networks to aggregate multimodal knowledge graph nodes of multihop adjacent nodes, respectively, to model user preferences and item high-order representations. Finally, the recommendation is made using the aggregated embedding representation. PMGT [19] is the pre-trained model that leverages the fused multimodal feature and interactions. The model is pre-trained by setting two graph reconstruction tasks, graph structure reconstruction and masked node feature reconstruction, as the learning objectives. TMKG [20] utilizes multitask learning to end-to-end integrate the two-side information of the trust graph and knowledge graph, in order to capture implicit relationships in a more fine-grained manner. DMRL [21] can capture the different modalities of users' attention to each factor in user preference modeling and adopts a decoupled representation technique to ensure that the features of different factors in each modality are independent of each other. MEGCF [7] captures the semantic relevance between user and item interactions and richer multimodal features by constructing a symmetric linear graph convolutional network, thereby achieving the synchronous capture of higher-order multimodal semantic relevance and collaborative signals. LUDP [22] uses modal information to construct modal similarity graphs, so as to mine potential relationships between items with similar patterns and learn deeper user preferences based on the characteristics of the different modes of interaction the user has had with items in the past. Furthermore, certain studies construct auxiliary graphs based on user or item relationships. For instance, DualGNN [23] develops a user co-occurrence graph to extract more precise representations of users by leveraging the interrelations across users. LATTICE [24] constructs item-item relation graphs for each modality utilizing multimodal features, subsequently fusing these graphs to derive a latent item-item graph, resulting in a more holistic latent item representation and augmenting recommendation performance. FREEDOM [25] is based on LATTICE, where the latent item-item graph updates are frozen, and a degree-sensitive edge pruning method is applied to reduce the noise in the interaction graph between users and items. Other approaches employed attention mechanisms to extract user preferences for items' multimodal features, hence enhancing recommendation performance. MGAT [26], built upon the MMGCN framework, uses the standard graph convolutional network (GCN) for aggregation and combines the aggregated results. To improve information flow across different modalities, it introduced a new gated attention mechanism. Despite the notable achievements of current multimodal recommendation methods, many limitations persist. Primarily, some approaches utilize the multimodal item features directly as supplementary information for evaluating user preferences [5; 14; 23]. In reality, the influence of these features on user preferences are variable. To address this issue, some methods use multimodal features as node representations. For instance, MMGCN [13] builds the user-item interaction graphs for each modality to derive the modality-specific representations of users and items, subsequently merging the GCN outputs from different modalities to achieve the final representations.