Summary of Symmetric Graph Contrastive Learning Against Noisy Views For Recommendation, by Chu Zhao et al.
Symmetric Graph Contrastive Learning against Noisy Views for Recommendation
by Chu Zhao, Enneng Yang, Yuliang Liang, Jianzhe Zhao, Guibing Guo, Xingwei Wang
First submitted to arxiv on: 3 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Graph Contrastive Learning (GCL) technique leverages data augmentation to produce contrasting views for enhanced recommendation system performance. By introducing noisy views that share limited information with the original graph, existing methods can lead to sub-optimal performance. The authors define these noisy views as those with a cosine similarity value less than 0.1 to the original view. They demonstrate that these noisy views significantly degrade recommendation performance and propose Symmetric Graph Contrastive Learning (SGCL) to address this issue. SGCL introduces symmetry theory into graph contrastive learning, providing a symmetric form and contrast loss resistant to noisy interference. The authors provide theoretical proof of SGCL’s high tolerance to noisy views and conduct extensive experiments on three real-world datasets. Results show that SGCL substantially increases recommendation accuracy, outperforming nine competing models with relative improvements reaching 12.25%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores a new approach to improving the performance of recommendation systems. By using data augmentation techniques, researchers can create multiple versions of the same information and then compare them to find patterns. The problem is that some of these versions might be “noisy” or incorrect, which can actually make things worse. To solve this issue, the authors propose a new method called Symmetric Graph Contrastive Learning (SGCL). SGCL is designed to ignore the noisy information and focus on the good stuff. This helps recommendation systems make better predictions about what people will like. The authors tested their approach on three different datasets and found that it works really well. |
Keywords
» Artificial intelligence » Cosine similarity » Data augmentation
