Abstract
The herb recommender system usually induces the implicit syndrome representations based on TCM prescriptions to generate related herbs as a treatment to cure a given symptom set. Previous methods primarily focus on modeling the interaction between symptoms (or diseases) and herbs without explicitly considering the syndrome information. As a result, these methods only capture the coarse-grained syndrome information. In this paper, we propose a new method to incorporate the explicit syndrome information for herb recommendation. To model the coarse-grained interaction between diseases and herbs within a specific syndrome class, we employ clustering algorithms to obtain the syndrome class, and apply the graph convolution network (GCN) on multiple disease-herb bipartite subgraphs. Next, we model the fine-grained interaction upon the syndrome-herb graph. Further, we propose a syndrome-aware heterogeneous graph neural network architecture, which integrates the syndrome information into the GCN message propagation process by combining the coarse-grained and fine-grained information of the interactions. The experimental results on the real TCM dataset demonstrate the improvements over state-of-the-art herb recommendation methods, further validate the effectiveness of our model.
Jiayin Huang and Wenjing Yue are co-first authors of the article.
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Acknowledgements
We thank editors and reviewers for their suggestions and comments. This work was supported by NSFC grants (No. 62136002), National Key R&D Program of China (2021YFC3340700) and Shanghai Trusted Industry Internet Software Collaborative Innovation Center.
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Huang, J., Yue, W., Wang, Y., Zhu, J., Ni, L. (2023). Syndrome-Aware Herb Recommendation with Heterogeneous Graph Neural Network. In: El Abbadi, A., et al. Database Systems for Advanced Applications. DASFAA 2023 International Workshops. DASFAA 2023. Lecture Notes in Computer Science, vol 13922. Springer, Cham. https://doi.org/10.1007/978-3-031-35415-1_8
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