SMRGAT: A traditional Chinese herb recommendation model based on a multi-graph residual attention network and semantic knowledge fusion-Reference-Cited by-同舟云学术

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SMRGAT: A traditional Chinese herb recommendation model based on a multi-graph residual attention network and semantic knowledge fusion

Published:2023-10 Issue: Volume:315 Page:116693
ISSN:0378-8741
Container-title:Journal of Ethnopharmacology
language:en
Short-container-title:Journal of Ethnopharmacology

Author:

Yang Xiaoyan,Ding Changsong^ORCID

Publisher

Elsevier BV

Subject

Drug Discovery,Pharmacology

Reference27 articles.

1. Top-k off-policy correction for a reinforce recommender system;Chen,2019

2. Heterogeneous information network based clustering for precision traditional Chinese medicine;Chen;BMC Med. Inf. Decis. Making,2019

3. Anti-tumor activities and mechanisms of traditional Chinese medicines formulas: a review;Fan;Biomed. Pharmacother.,2020

4. Lightgcn: simplifying and powering graph convolution network for recommendation;He,2020

5. Syndrome-aware herb recommendation with multi-graph convolution network;Jin,2020

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. An enhanced graph convolutional network with property fusion for acupoint recommendation;Applied Intelligence;2024-08-26

2. MDAR: A Knowledge-Graph-Enhanced Multi-Task Recommendation System Based on a DeepAFM and a Relation-Fused Multi-Gead Graph Attention Network;Applied Sciences;2023-07-27

3. Multiple prescription pattern recognition model based on Siamese network;Mathematical Biosciences and Engineering;2023

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