Towards user-specific multimodal recommendation via cross-modal attention-enhanced graph convolution network

Multimodal Recommendation (MR) exploits multimodal features of items (e.g., visual or textual features) to provide personalized recommendations for users. Recently, scholars have integrated Graph Convolutional Networks (GCN) into MR to model complicated multimodal relationships, but still with two significant challenges: (1) Most MR methods fail to consider the correlations between different modalities, which significantly affects the modal alignment, resulting in poor performance on MR tasks. (2) Most MR methods leverage multimodal features to enhance item representation learning. However, the connection between multimodal features and user representations remains largely unexplored. To this end, we propose a novel yet effective Cross-modal Attention-enhanced graph convolution network for user-specific Multimodal Recommendation, named CAMR. Specifically, we design a cross-modal attention mechanism to mine the cross-modal correlations. In addition, we devise a modality-aware user feature learning method that uses rich item information to learn user feature representations. Experimental results on four real-world datasets demonstrate the superiority of CAMR compared with several state-of-the-art methods. The codes of this work are available at https://github.com/ZZY-GraphMiningLab/CAMR