Abstract
In common graph neural network (GNN), although incorporating social network information effectively utilizes interactions between users, it often overlooks the deeper semantic relationships between items and fails to integrate visual and textual feature information. This limitation can restrict the diversity and accuracy of recommendation results. To address this, the present study combines knowledge graph, GNN, and multimodal information to enhance feature representations of both users and items. The inclusion of knowledge graph not only provides a better understanding of the underlying logic behind user interests and preferences but also aids in addressing the cold-start problem for new users and items. Moreover, in improving recommendation accuracy, visual and textual features of items are incorporated as supplementary information. Therefore, a user recommendation model is proposed that integrates hierarchical graph attention network with multimodal knowledge graph. The model consists of four key components: a collaborative knowledge graph neural layer, an image feature extraction layer, a text feature extraction layer, and a prediction layer. The first three layers extract user and item features, and the recommendation is completed in the prediction layer. Experimental results based on two public datasets demonstrate that the proposed model significantly outperforms existing recommendation methods in terms of recommendation performance.