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Graph Augmentation for Recommendation
March 26, 2024, 4:42 a.m. | Qianru Zhang, Lianghao Xia, Xuheng Cai, Siuming Yiu, Chao Huang, Christian S. Jensen
cs.LG updates on arXiv.org arxiv.org
Abstract: Graph augmentation with contrastive learning has gained significant attention in the field of recommendation systems due to its ability to learn expressive user representations, even when labeled data is limited. However, directly applying existing GCL models to real-world recommendation environments poses challenges. There are two primary issues to address. Firstly, the lack of consideration for data noise in contrastive learning can result in noisy self-supervised signals, leading to degraded performance. Secondly, many existing GCL approaches …
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