Research map / Learning theory and algorithms / Matrix and tensor methods
Confidence-Aware Matrix Factorization for Recommender Systems
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add per-user and per-item variance parameters (CBPMF) | BPMF · MovieLens 90% interval | coverage percentage gap to level: 86.26% -> 89.24% | Helped |
| add per-user and per-item variance parameters (CBPMF) | BPMF · Netflix 90% interval | coverage percentage gap to level: 82.16% -> 86.48% | Helped |
| add per-user and per-item variance parameters (CBPMF) | BPMF · Jester 90% interval | coverage percentage gap to level: 78.06% -> 86.57% | Helped |
| add per-user and per-item variance parameters (CBPMF) | BPMF · Jester 95% interval | coverage percentage gap to level: 84.15% -> 92.56% | Helped |
| add per-user and per-item variance parameters to PMF/BPMF | PMF and BPMF · MovieLens, Netflix, Jester | RMSE | Helped |
| rerank candidates by Sharpe ratio (Confidence-aware Ranking) | CBPMF · MovieLens, Netflix, Jester | NDCG@K and AP@K | Helped |
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