Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Bayesian Deep Collaborative Matrix Factorization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| full Bayesian posterior estimation (BDCMF-2) instead of point estimation (BDCMF-1) (ablation) | BDCMF · Lastfm | Recall@20: 10.4% relative improvement | Helped |
| point estimation trained beyond 30 iterations (ablation) | BDCMF-1 · Lastfm | Recall@20 | Hurt |
| incorporate neural-network content model into Bayesian generative MF vs PoissonMF-CS | BDCMF-1 · Lastfm | Recall@50: 0.2730 -> 0.3026 | Helped |
| BDCMF-1 vs NeuMF | BDCMF-1 · Delicious | Recall@50: 0.2267 -> 0.2341 | Helped |
| add content and social information (hybrid) vs PMF | MF · Lastfm | Recall@50: 0.1328 -> 0.3026 | Helped |
| increase content weight lambda_v above 10 (ablation) | BDCMF-1 · Lastfm | Recall@50 | Hurt |
| vary latent dimension D (ablation) | BDCMF-1 · Lastfm and Delicious | recommendation performance | No clear effect |
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