TCM Intelligent Recommendation Based on Multimodal Knowledge Graph Attention Network

In recent years, with the inheritance, innovation, and development of traditional Chinese medicine (TCM), an increasing number of individuals have shown interest in the field of TCM. However, TCM, as a complex and rich medical system, has posed challenges in terms of information retrieval and dissemination. Traditional search engine methods suffer from issues such as low retrieval efficiency and information overload, which significantly hinder the informatization process of TCM. To address this problem, we propose a Multimodal Knowledge Graph Attention Networks (MKGAN) to enhance TCM intelligent recommendation by leveraging the multimodal knowledge of TCM and their high-order side information. Specially, we construct a multimodal knowledge graph (MKG) for TCM by integrating data from various heterogeneous sources into a unified knowledge representation. Building upon this MKG, we employ graph attention networks to enhance both item and user feature representations. Ultimately, we utilize these enhanced feature representations to achieve personalized recommendations. Extensive experiments and performance comparisons with baseline models demonstrate the superior performance of MKGAN in TCM intelligent scientific recommendation, which promises substantial support for TCM informatization process.