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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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