Research map / Label-efficient and robust learning / Recognition and long-tail
Explainable Recommendation through Attentive Multi-View Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| attentive view combination instead of equal view weights (ablation) | DEAML · Digital Music | RMSE: 0.9190 -> 0.9118 | Helped |
| attentive view combination instead of equal view weights (ablation) | DEAML · Yelp | RMSE: 1.1343 -> 1.1333 | No clear effect |
| attentive multi-view hierarchical learning instead of single-view EFM | EFM · Toys and Games | RMSE: 0.9534 -> 0.9040 | Helped |
| add co-regularization loss (lambda_v=10 vs 0) (ablation) | DEAML · Digital Music | RMSE | Helped |
| add per-view rating loss (lambda_a=3 vs 0) (ablation) | DEAML · Digital Music | RMSE | Helped |
| more latent factors (ablation) | DEAML · Digital Music | RMSE | No clear effect |
| hierarchical constrained explanation selection instead of EFM explanations | explanation generation · Yelp user study | usefulness score: 3.16 -> 3.63 | Helped |
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