Research map / Learning theory and algorithms / Matrix and tensor methods
Scalable Demand-Aware Recommendation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Model category-specific inter-purchase durations alongside form utility and account for positive-unlabeled purchases | DAROSS · Tmall and Amazon Review subsets, category prediction | Average category ranking percentage | Helped |
| Model category-specific inter-purchase durations alongside form utility and account for positive-unlabeled purchases | DAROSS · Tmall and Amazon Review subsets, purchase time prediction | Average time error percentage | Helped |
| Account for positive-unlabeled feedback instead of treating implicit purchases like explicit ratings | WR-MF compared with M³F and PMF · Tmall and Amazon Review subsets, category prediction | Average category ranking percentage | Helped |
| Use demand-aware, time-sensitive recommendations rather than static collaborative filtering | DAROSS compared with WR-MF and PMF · Amazon Review full, item prediction | Average item ranking percentage: 16.7% vs 27.3% and 38.4% | Helped |
| Relax the time-varying form-utility tensor to a time-invariant matrix (ablation) | DAROSS · Real-world recommendation experiments | Prediction performance | Mixed |
| Flip observed purchase-tensor entries from 0 to 1 to simulate noisy purchases | Synthetic DAROSS · Synthetic duration recovery | Relative duration prediction error | Hurt |
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