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Symmetric Graph Contrastive Learning against Noisy Views for Recommendation

Autor: Zhao, Chu, Yang, Enneng, Liang, Yuliang, Zhao, Jianzhe, Guo, Guibing, Wang, Xingwei

Graph Contrastive Learning (GCL) leverages data augmentation techniques to produce contrasting views, enhancing the accuracy of recommendation systems through learning the consistency between contrastive views. However, existing augmentation methods,

Externí odkaz: http://arxiv.org/abs/2408.02691

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Graph Representation Learning via Causal Diffusion for Out-of-Distribution Recommendation

Autor: Zhao, Chu, Yang, Enneng, Liang, Yuliang, Lan, Pengxiang, Liu, Yuting, Zhao, Jianzhe, Guo, Guibing, Wang, Xingwei

Graph Neural Networks (GNNs)-based recommendation algorithms typically assume that training and testing data are drawn from independent and identically distributed (IID) spaces. However, this assumption often fails in the presence of out-of-distribut

Externí odkaz: http://arxiv.org/abs/2408.00490

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Akademický článek

ORTHOGONAL TEST OPTIMUM RESEARCH FOR THE BOTTOM SLEW PLATFORM OF BELT CONVEYER

Autor: LIAO XiangHui, WEI ShiLong, LIU Huan, ZHAO Chu, XI MingLong

Publikováno v: Jixie qiangdu, Vol 37, Pp 456-460 (2015)

A orthogonal test optimum design methodology was applied in the intensity analysis of shuttle belt conveyer slewing bearing,the finite element analysis was used to analyze the structure of the belt conveyer’s lower supported structure in the condit

Externí odkaz: https://doaj.org/article/76e5cb8cf88d4dbab83f1c31f2a749dd