An enhanced graph convolutional network with property fusion for acupoint recommendation

Acupuncture therapy, rooted in traditional Chinese medicine (TCM), plays a pivotal role in both disease treatment and preventive health care. A significant challenge within this realm is precise acupoint recommendations tailored to specific symptoms, with consideration of the intricate inherent relationships between the symptoms and acupoints. Traditional recommendation methods encounter another difficulty in grappling with the sparse nature of TCM data. To address these issues, we present a novel approach called the enhanced graph convolutional network with property fusion (PEGCN), which consists of two key components, the property feature graph encoder module and the enhanced graph convolutional network module. The former extracts property knowledge of acupoints to enrich their representations. The latter integrates the GCN structure and an attention mechanism to efficiently capture the underlying relationships between symptoms and acupoints. In this paper, we apply the PEGCN model to a real-world dataset related to acupuncture therapy, and the experimental results demonstrate its superiority over the baseline models in terms of the evaluation metrics, which include Precision@K, Recall@K, and NDCG@K. This finding suggests that our model effectively addresses the challenges associated with acupoint recommendations, offering an improved method for personalized treatments in the TCM context.