Research map / Graph learning / Knowledge and temporal graphs
Multi-Component Graph Convolutional Collaborative Filtering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add node-level attention (ablation) | MCCF · Yelp | RMSE: 0.3806 -> 0.3836 | No clear effect |
| add node-level attention (ablation) | MCCF · Amazon | RMSE: 0.8942 -> 0.8876 | Helped |
| add node-level attention (ablation) | MCCF · MovieLens-100k | RMSE: 0.9203 -> 0.9070 | Helped |
| add node-level attention (ablation) | MCCF · Yelp | MAE: 0.1029 -> 0.1286 | No clear effect |
| add node-level attention (ablation) | MCCF · Amazon | MAE: 0.6595 -> 0.6428 | Helped |
| add node-level attention (ablation) | MCCF · MovieLens-100k | MAE: 0.7160 -> 0.7050 | Helped |
| add component-level attention (ablation) | MCCF · Yelp | RMSE: 0.3806 -> 0.3806 | No clear effect |
| add component-level attention (ablation) | MCCF · Amazon | RMSE: 0.8919 -> 0.8876 | Helped |
| add component-level attention (ablation) | MCCF · MovieLens-100k | RMSE: 0.9142 -> 0.9070 | Helped |
| add component-level attention (ablation) | MCCF · Yelp | MAE: 0.1029 -> 0.1029 | No clear effect |
| add component-level attention (ablation) | MCCF · Amazon | MAE: 0.6483 -> 0.6428 | Helped |
| add component-level attention (ablation) | MCCF · MovieLens-100k | MAE: 0.7081 -> 0.7050 | Helped |
Closest papers by meaning
- Hgformer: Hyperbolic Graph Transformer for Collaborative Filtering (ICML 2025 · similarity 0.88)
- Beyond Graph Convolution: Multimodal Recommendation with Topology-aware MLPs (AAAI 2025 · similarity 0.87)
- TV-Rec: Time-Variant Convolutional Filter for Sequential Recommendation (NeurIPS 2025 · similarity 0.87)
- Semantic Enhanced Heterogeneous Hypergraph Network for Collaborative Filtering (AAAI 2025 · similarity 0.87)
- LGMRec: Local and Global Graph Learning for Multimodal Recommendation (AAAI 2024 · similarity 0.86)
- DOGE: LLMs-Enhanced Hyper-Knowledge Graph Recommender for Multimodal Recommendation (AAAI 2025 · similarity 0.86)
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