Research map / Federated learning and privacy / Privacy attacks and secure learning
PPGenCDR: A Stable and Robust Framework for Privacy-Preserving Cross-Domain Recommendation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use PPGenCDR instead of PriCDR | PPCDR · Douban book → music | HR@5: .3074 -> .3701 | Helped |
| Use PPGenCDR instead of PriCDR | PPCDR · Amazon music → book | HR@5: .5605 -> .6012 | Helped |
| Use PPGenCDR instead of PriCDR | PPCDR · Douban movie → book | HR@5: .3577 -> .4238 | Helped |
| Use PPGenCDR instead of PriCDR | PPCDR · Amazon movie → book | HR@5: .5862 -> .6253 | Helped |
| Use PPGenCDR instead of PriCDR | PPCDR · Douban movie → music | HR@5: .3346 -> .3782 | Helped |
| Use PPGenCDR instead of PriCDR | PPCDR · Amazon movie → music | HR@5: .6015 -> .6379 | Helped |
| Add GAN stabilizer (ablation) | PPGenCDR · Douban book → music | HR@5: .2944 -> .3701 | Helped |
| Add GAN stabilizer (ablation) | PPGenCDR · Amazon music → book | HR@5: .5701 -> .6012 | Helped |
| Add GAN stabilizer (ablation) | PPGenCDR · Douban movie → book | HR@5: .3647 -> .4238 | Helped |
| Add robustness conductor (ablation) | PPGenCDR · Amazon movie → book | HR@5: .3614 -> .6253 | Helped |
| Add robustness conductor (ablation) | PPGenCDR · Douban movie → music | HR@5: .2739 -> .3782 | Helped |
| Apply RDP selectively to the discriminator gradients rather than the whole model (ablation) | PPGenCDR · Amazon movie source | HR: .1098 -> .0945 | Hurt |
Closest papers by meaning
- Reducing Item Discrepancy via Differentially Private Robust Embedding Alignment for Privacy-Preserving Cross Domain Recommendation (ICML 2024 · similarity 0.91)
- Federated Graph Learning for Cross-Domain Recommendation (NeurIPS 2024 · similarity 0.85)
- RRL: Recommendation Reverse Learning (AAAI 2024 · similarity 0.84)
- RAPID: Retrieval Augmented Training of Differentially Private Diffusion Models (ICLR 2025 · similarity 0.83)
- Domain-Level Disentanglement Framework Based on Information Enhancement for Cross-Domain Cold-Start Recommendation (AAAI 2025 · similarity 0.83)
- Graph Disentangled Contrastive Learning with Personalized Transfer for Cross-Domain Recommendation (AAAI 2024 · similarity 0.83)
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