Multimodal heterogeneous graph convolutional network for image recommendation

To improve the efficiency of the connection between people and information in specific scenarios, recent work has focused on mining user preferences from interactions. However, with the emergence of multimodal information in recent years, user choice in the image recommendation domain is influenced by multiple factors, such as image style, tags, and user social relationships, etc. Therefore, to explore user preferences under different modalities, we capture potential user preferences in a multimodal collaborative manner. In this work, a multimodal heterogeneous graph convolutional network model for image recommendation is proposed, which explores the differences in the representation of user preferences under different modalities. For different modalities, deep propagation networks are employed to construct higher-order connectivity coding between user heterogeneous interactions and image, tag, and user preference information. In addition, a dual-channel attention strategy with the idea of partitioning is employed to optimize the potential preferences of users. The experiments are conducted on public real-world datasets, the results clearly demonstrate the collaborative ability of multimodal information and heterogeneous interaction relations in exploring user preferences.