# UPGCN: User Perception-Guided Graph Convolutional Network for Multimodal Recommendation
## 5 Conclusions  
In this paper, we have proposed a user perception-guided graph convolutional network for multimodal recommendation. Specifically, we developed a user perception-guided representation enhancement module, which initially employs various modal features of the item as inputs, conducts GCN on the interaction graph between users and items to derive user representations across different modalities, and subsequently utilizes these representations as guidance signals to enhance the embedding of user ID, thereby obtaining a more accurate user representation by embedding learning. Furthermore, in order to effectively separate the similarity between different modalities and highlight the unique features of each modality while fully utilizing their complementarity, we design a multimodal two-step enhanced fusion method that takes a \"first enhancement and then fusion\" strategy for multimodal features to obtain a more accurate item representation for recommendation prediction. We conducted extensive experiments on three real-world datasets to evaluate the effectiveness of the proposed model in the recommendation task. The experimental results show that the proposed model outperforms the baseline model in terms of recom  
Figure 4: The effects of weight for multimodal BPR loss. mendation performance. Furthermore, we believe that UPGCN can be extended to other domains, such as the short video domain. In current collaborative filtering methods, the relationships between users and items are primarily used to identify items of interest to users. However, items typically possess multimodal features, such as images and reviews for products, as well as audio, video images, and comments in short videos. As long as the initial work effectively extracts the multimodal features of items using deep learning techniques, our model can utilize these features along with user-item historical interaction data as input, leading to effective recommendations. This is also one of the directions we aim to pursue in the future. While UPGCN effectively uses multimodal features, it assumes that all modalities are available and of high quality. However, in real-world scenarios, certain modalities might be missing or noisy. Future work could address this limitation by developing methods to handle incomplete or unreliable multimodal data, perhaps through modality dropout or denoising mechanisms. Furthermore, the UPGCN model primarily focuses on the accuracy of recommendations but does not fully consider the efficiency of recommendations. The model's graph convolutional structure, combined with the multimodal feature fusion, can lead to higher computational costs, particularly when scaling to very large graphs. Specifically, our model may involve more machine learning processes related to graphs, including user-item interaction graphs, item-item graphs in each modality, and latent item-item graphs, and the graph involved in real-world applications is much larger, which would result in higher computational costs when performing graph convolution operations on these large graphs. In future work, we could focus on optimizing the architecture to improve efficiency without sacrificing recommendation quality, perhaps through more lightweight graph processing techniques, pruning methods, or knowledge distillation techniques [45]. Conceptualization, B.Z. ; methodology, B.Z. ; software, B.Z. ; validation, B.Z. ; formal analysis, B.Z. ; investigation, B.Z. ; resources, Y.L. ; data curation, Y.L. ; writing--original draft preparation, B.Z. ; writing--review and editing, B.Z. and Y.L. ; visualization, B.Z. ; supervision, Y.L. ; project administration, Y.L. ; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Data availability Statement: Data are available in a publicly accessible repository.