Summary of Your Graph Recommender Is Provably a Single-view Graph Contrastive Learning, by Wenjie Yang et al.
Your Graph Recommender is Provably a Single-view Graph Contrastive Learning
by Wenjie Yang, Shengzhong Zhang, Jiaxing Guo, Zengfeng Huang
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 In this paper, researchers explore the connection between Graph Recommender (GR) and Graph Contrastive Learning (GCL), both of which are graph neural network (GNNs) encoders designed for user-item interaction graphs. The study reveals that GCL can be used as a general graph representation learning method to jointly train GRs with supervised recommendation loss, leading to improved performance on the recommendation task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how Graph Recommender (GR) and Graph Contrastive Learning (GCL) work together. GR is a type of neural network that helps recommend items based on user interactions. GCL is another type of neural network that can learn from graph data without needing labeled examples. Researchers found that GCL can be used to improve the performance of GR, making it better at recommending items. |
Keywords
» Artificial intelligence » Graph neural network » Neural network » Representation learning » Supervised
