A novel multi-view contrastive learning for herb recommendation

Herb recommendation plays a crucial role in Traditional Chinese Medicine (TCM) by prescribing therapeutic herbs given symptoms. Current herb recommendation methods make promising progress by utilizing graph neural network, but most fail to capture the underlying distribution of the prescription, particularly the long-tailed distribution, and then suffer from cold-start and data sparsity problems such as emerging epidemics or rare diseases. To effectively alleviate these problems, we first propose a novel multi-view contrastive learning method to improve the prediction performance as contrastive learning can derive self-supervision signals from raw data. For exploiting structural and semantic relationship of symptoms and herbs, we construct the symptom-herb graph from inter-view, and the symptom-symptom interactions and the herb-herb interactions from intra-view. From inter-view, we present a new dual structural contrastive learning that adds and drops data depending on the frequency of the prescription dataset to exploit unbalanced data distribution rather than traditional data augmentation. From intra-view, we propose multi-level semantic contrastive learning depending on the co-occurrence frequencies of symptoms and herbs respectively for utilizing various correlations based on statistical results. Experiments conducted on real-world datasets demonstrate the superiority of the proposed method, which improves performance, and robustness against data sparsity.