基于自适应阈值滤波的GCN推荐系统

Meng Qiao, Hairen Gui, Ke Tang
{"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}
引用次数: 0

摘要

介绍了自适应阈值滤波图卷积神经网络模型(AT-GCN)。AT-GCN是一种基于图结构的推荐模型。与常用的图结构推荐模型相比,AT-GCN能有效地解决边缘表示和信息传递问题,提高推荐效果。实验部分在AT-GCN上进行了几组实验,最后通过实验结果验证了上述结论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Improve vulnerability prediction performance using self-attention mechanism and convolutional neural network Design of digital pulse-position modulation system based on minimum distance method Design of an externally adjustable oscillator circuit Research on non-intrusive video capture technology based on FPD-linkⅢ The communication process of digital binary pulse-position modulation with additive white Gaussian noise
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1