{"title":"基于自适应阈值滤波的GCN推荐系统","authors":"Meng Qiao, Hairen Gui, Ke Tang","doi":"10.1117/12.2639323","DOIUrl":null,"url":null,"abstract":"We introduce the AT-GCN (Adaptive Threshold filtering Graph Convolutional Neural network model). AT-GCN is a recommendation model based on graph structure. Compared with the commonly used graph structure recommendation model, AT-GCN can effectively solve the problem of edge representation and information transfer, and improve the recommendation effect. In the experimental part, several groups of experiments were carried out on AT-GCN, and the above conclusions were finally verified by the experimental results.","PeriodicalId":336892,"journal":{"name":"Neural Networks, Information and Communication Engineering","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Recommender system based on adaptive threshold filtering GCN\",\"authors\":\"Meng Qiao, Hairen Gui, Ke Tang\",\"doi\":\"10.1117/12.2639323\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We introduce the AT-GCN (Adaptive Threshold filtering Graph Convolutional Neural network model). AT-GCN is a recommendation model based on graph structure. Compared with the commonly used graph structure recommendation model, AT-GCN can effectively solve the problem of edge representation and information transfer, and improve the recommendation effect. In the experimental part, several groups of experiments were carried out on AT-GCN, and the above conclusions were finally verified by the experimental results.\",\"PeriodicalId\":336892,\"journal\":{\"name\":\"Neural Networks, Information and Communication Engineering\",\"volume\":\"16 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-06-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neural Networks, Information and Communication Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1117/12.2639323\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neural Networks, Information and Communication Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2639323","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Recommender system based on adaptive threshold filtering GCN
We introduce the AT-GCN (Adaptive Threshold filtering Graph Convolutional Neural network model). AT-GCN is a recommendation model based on graph structure. Compared with the commonly used graph structure recommendation model, AT-GCN can effectively solve the problem of edge representation and information transfer, and improve the recommendation effect. In the experimental part, several groups of experiments were carried out on AT-GCN, and the above conclusions were finally verified by the experimental results.