# UPGCN: User Perception-Guided Graph Convolutional Network for Multimodal Recommendation
## 1 Introduction  
The swift advancement of the internet has facilitated the accessibility of multimedia content in our daily lives. Consequently, the extensive volume of multimedia content, particularly within e-commerce enterprises, has led to the problem of information overload. Recommendation systems are essential for efficiently using the vast array of multimedia content in e-commerce, assisting consumers in identifying products or services of interest among millions of offerings. Collaborative filtering (CF) is a traditional method in recommendation systems that generates personalized suggestions by analyzing the similarities in user behaviors. It has been thoroughly advanced in the domain of recommendations. For instance, Bayesian Personalized Ranking (BPR) [1], a widely recognized collaborative filtering technique that enhances user representation and item representation through matrix factorization, utilizes a BPR loss for optimization. The method of neural graph collaborative filtering (NGCF) [2] integrates user-item interactions into the embedding process, connecting users and items through higher-order connectivity, and utilizes graph convolutional networks (GCNs) to extract valuable information from each layer. MM-Rec [3] combines textual and visual information from news articles to learn multimodal news representations. LightGCN [4] enhances the information aggregation technique of the original GCN, simplifying the model while augmenting recommendation performance. Collaborative filtering's advantage rests in its independence from prior knowledge regarding item or user attributes, as it utilizes user behavior information to generate recommendations. However, it has other limitations and challenges, such as the cold start problem and data sparsity. As e-commerce continues to advance rapidly, the accessibility of multimodal item features (including images, text, and videos) has surged. Numerous researchers have integrated these multimodal features into recommendation methods as supplementary information for items, effectively addressing the cold start issue and data sparsity. Consequently, multimodal recommendation methods that incorporate multimodal features into conventional collaborative filtering methods have arisen. Early multimodal recommendation methods [4; 5] regarded multimodal features as supplementary information for items, with the objective of mining users' preferences for those items. For example, Visual Bayesian Personalized Ranking (VBPR) [5] employs a convolutional neural network (CNN) to derive visual features from images, subsequently integrating these features with item ID embeddings to create a complete item representation. The extensive utilization of graph neural networks (GNNs) in recommendation methods [6; 7; 8; 9; 10; 11; 12] has led to the adoption of GNNs in multimodal recommendation methods to improve the performance of multimodal recommendation. For instance, MMGCN [13] employs three distinct modalities of micro-video information (visual, acoustic, and textual) to construct user-item interaction graphs across various modalities. Graph convolutional networks are then utilized for message passing to derive embedding representations of users and items in these modalities, which are then concatenated to produce the final user preference prediction. GRCN [14] dynamically modifies and enhances the configuration of the user-item interaction graph according to the model training status, thereby removing extraneous edges and acquiring more pertinent information regarding user preferences. EgoGCN [15] uses edgewise modulation (EGO) fusion technology to adaptively transfer multimodal information on the boundary, solving the problem of insufficient fusion or noise information in existing methods. EgoGCN is different from other graph fusion methods because it improves the transfer of information between modalities by changing the boundary information of local modalities. This keeps the processing within modalities and lets information flow between multiple modalities.