# UPGCN: User Perception-Guided Graph Convolutional Network for Multimodal Recommendation
## 1 Introduction
Despite employing analogous methodologies that consider the user's individual interests across several modalities, they just utilize GCNs within parallel interaction graphs and uniformly process all nearby information, failing to adaptively capture user modality preferences and ignoring the mutual relationships between different modalities, while neglecting the similarity and complementarity between the features of items in different modalities in the embedding vector fusion. In this paper, we present a user perception-guided graph convolutional network for multimodal recommendation, referred to as UPGCN. Specifically, in the UPGCN model, we first use the multimodal features of items as input and perform graph convolution on the user-item interaction graph to extract user representations under different modalities. Then, we further use the output user representations and the user-item interaction graph to mine the item representations. To accurately assess the potential impact of item features across various modalities on user preferences, we developed a user perception guided representation enhancement module (UPEM), which utilizes user representation with modal preferences as a guiding signal to augment the user ID Embedding. Additionally, to effectively utilize the similarity and complementarity between different modalities of the items' features, a two-step enhanced fusion method was designed, in which the original modalities of item features and the modalities of item features with user modal preferences were used as enhanced signals to enhance the item ID Embedding. Finally, the enhanced user ID Embedding and item ID Embedding are learned through LightGCN to obtain the final user preference prediction. In addition, we chose graph convolutional networks (GCNs) as the basis of our method because of their effectiveness in handling graph structured data, especially in modeling user-item interactions in recommendation systems. GCNs can capture complex user-item relationships by aggregating information from neighboring nodes, thereby improving the accuracy of recommendations. The main contributions of our work can be outlined as follows:  
* A user perception-guided graph convolutional network for multimodal recommendation is proposed, which combines the multimodal features of items into the GCN model and utilizes the user-item interaction graph and GCN to extract the modal preferences of users better. It obtains a more accurate user representation and effectively fuses the item representation in different modalities. * A user perception-guided representation enhancement module (UPEM) is developed, which first utilizes the different modal features of the item as inputs, performs graph convolution on the user-item interaction graph to obtain user representations in different modalities, and then utilizes the user representation as a guidance signal to augment the embedding of user ID, thus obtaining a more accurate user representation. * A multimodal two-step enhanced fusion method is proposed, which extracts useful information from item features with user modality preference and the original item's multimodal features for enhancing item representation. * We performed comprehensive experiments on three public datasets to demonstrate the effectiveness of our proposed model. The rest of this paper is organized as follows. We introduce related work in Section 2. Section 3 presents a model of the user perception-guided graph convolutional network for multimodal recommendation. In Section 4, we provide a detailed explanation of the experimental design, including the datasets used, evaluation metrics, and parameter settings, along with comparative experiments and results analysis against baseline methods. Additionally, ablation experiments and their results analysis, as well as experiments related to hyperparameters, are also conducted. Section 5 summarizes the model and method we have proposed and points out the future research goals.