Homogeneous Symptom Graph Attentive Reasoning Network for Herb Recommendation

The herb recommendation system aiming for recommending a set of herb for patients is a significant task for Traditional Chinese Medicine (TCM). Recent works apply a graph convolutional network to model the relations among symptoms and herbs, showing promising performance. However, they typically suffer from two limitations: (1) The learning of the relations of symptoms and herbs from symptom-herb heterogeneous graphs would be disturbed by the semantic gap and the weak correlations between symptoms and herbs. (2) They ignore the complex diagnosis and systemic relations of a patient's multi-symptom, resulting in the lack of effectiveness and personalization in syndrome diagnosis. To overcome these limitations, we propose a novel Homogeneous Symptom Graph Attentive Reasoning Network (HSGARN). Firstly, to alleviate the noisy semantic gap and weak correlations of heterogeneous graphs, we propose a homogeneous graph embedding module to comprehensively model the semantic relations of symptoms and herbs. Secondly, we propose a symptom attentive reasoning module to generate syndrome representation for patients, which can sufficiently exploit the interrelation of a patient's symptoms and model the individual difference. Experimental results on two TCM datasets demonstrate the advantages of HSGARN over the state-of-the-arts.