Research map / Graph learning / Graph algorithms and clustering
Scalable Probabilistic Matrix Factorization with Graph-Based Priors
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Remove contested edges from graph side-information | GRALS-based matrix factorization · Flixster (3k) | RMSE: 0.9152 -> 0.8857 | Helped |
| Remove contested edges from graph side-information | GRALS-based matrix factorization · Douban (3k) | RMSE: 0.7504 -> 0.7497 | Hurt |
| Remove contested edges from graph side-information | GRALS-based matrix factorization · MovieLens 100k | RMSE: 0.9178 -> 0.9174 | Helped |
| Remove contested edges from graph side-information | PMF · Epinions | RMSE: 0.31 -> 0.28 | Helped |
| Remove contested edges from graph side-information | GRALS-based matrix factorization · Yahoo Music | RMSE: 22.760 -> 22.795 | Hurt |
| Remove contested edges from graph side-information | GRALS · MovieLens 20M (10-NN) | RMSE: 0.7898 -> 0.7894 | Helped |
| Remove contested edges from graph side-information | GRALS · MovieLens 20M (20-NN) | RMSE: 0.7925 -> 0.7895 | Helped |
| Remove contested edges from graph side-information | GRALS · MovieLens 20M (40-NN) | RMSE: 0.7922 -> 0.7887 | Helped |
| Use the GRAEM-updated graph instead of the original graph | KPMF · Douban (3k) | RMSE: 0.7324 -> 0.7323 | Helped |
| Use the GRAEM-updated graph instead of the original graph | KPMF · Flixster (3k) | RMSE: 0.9212 -> 0.9212 | No clear effect |
| Increase nearest-neighbour graph size from 10 to 40 | GRALS · MovieLens 20M | RMSE: 0.7898 -> 0.7922 | Hurt |
| Increase nearest-neighbour graph size from 10 to 40 | GPMF / GRAEM · MovieLens 20M | RMSE: 0.7894 -> 0.7887 | Helped |
Closest papers by meaning
- Bayesian Matrix Completion via Adaptive Relaxed Spectral Regularization (AAAI 2016 · similarity 0.87)
- Connectivity Shapes Implicit Regularization in Matrix Factorization Models for Matrix Completion (NeurIPS 2024 · similarity 0.87)
- Constrained Factorization with Diagonal Scaling: Rank-Revealing Training and Pruning (NeurIPS 2026 · similarity 0.87)
- Dictionary Learning for Massive Matrix Factorization (ICML 2016 · similarity 0.87)
- Provable Acceleration of Nesterov's Accelerated Gradient for Asymmetric Matrix Factorization and Linear Neural Networks (NeurIPS 2024 · similarity 0.86)
- In-depth Analysis of Low-rank Matrix Factorisation in a Federated Setting (AAAI 2025 · similarity 0.86)
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