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[PaperReview] LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
- 1. 박지민 kpdpkp@gmail.com LightGCN 1 : Simplifying and Powering Graph Convolution Network for Recommendation
- 2. Why GCN works well for CF?
- 3. NGCF Recap
- 5. Non Linear Activation Feature Transformation NGCF Component (2)
- 6. Non Linear Activation Feature Transformation NGCF largely follows Semi-Supervised Classification with Graph Convolutional Networks
- 7. Semi-Supervised Classification with Graph Convolutional Networks Rich semantic features (e.g. titles, abstract words) What About CF situation?
- 8. Ablation Study - NGCF-n : w/o Non-Linear Activation - NGCF-f : w/o Feature Transformation - NGCF-fn: w/o Non-Linear + w/o Feature Transformation
- 9. “we claim that when designing model for recommendation, it is important to perform rigorous ablation studies to be clear about the impact of each operation. Otherwise, including less useful operations will complicate the model unnecessarily, increase the training di ffi culty, and even degrade model e ff ectiveness.” “the deterioration of NGCF stems from the training di ffi culty, rather than over fi tting” Ablation Study - Conclusion
- 10. Use Essential Ingredient Component of GCN with Rationale
- 11. No non-linear! No feature trans! 🤔 No self-connection?
- 12. Concat => Weighted Sum This Captures Self-Connection.
- 13. Summary - Matrix Form
- 14. Rationale(1) - Self-Connection is implied. Define : Then : By Binomial Theorem Same as previous slide :
- 15. Rationale(1) - Self-Connection is implied. Define : Then : By Binomial Theorem Same as previous slide : Actually, This comes from “Simplifying Graph Convolutional Networks”
- 16. Rationale(2) - LightCGN combat oversmoothing. Define : Then : Same as previous slide :
- 17. Rationale(2) - LightCGN combat oversmoothing. Define : Then : Same as previous slide : “Predict then propagate: Graph neural networks meet personalized pagerank”
- 18. Rationale(3) - Its interpretability says it is CF. When is big? Cv→u
- 19. Training Recipe - Loss : BPR - Optimizer : Adam - Do not use Dropout - Layer Combination coefficient: 1/(K + 1)
- 20. Experiments - Comparison with NGCF - Comparison with SOTA - Ablation Studies - Impact of Layer Combination - Impact of Symmetric Sort Normalization - Analysis of Embedding Smoothness - Hyper-Parameter Study
- 23. Ablation Studies - Impact of Layer Combination 1. Focus on LightGCN-single 2. Focus on LightGCN 3. Compare
- 24. Ablation Studies - Symmetric Sort Normalization à = D−1 2 AD−1 2 => Removing Square L1
- 25. Ablation Studies - Analysis of Embedding Smoothness
- 28. Furthermore(2) Embarrassingly Shallow Autoencoders for Sparse Data