Research map / Label-efficient and robust learning / Recognition and long-tail
Towards Open-World Recommendation: An Inductive Model-based Collaborative Filtering Approach
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use IDCF-GC instead of IGMC for new-user recommendation | Open-world recommendation · ML-100K new users | RMSE: 1.051 -> 1.011 | Helped |
| Use IDCF-GC instead of IGMC for new-user recommendation | Open-world recommendation · ML-1M new users | RMSE: 0.997 -> 0.957 | Helped |
| Use IDCF-GC instead of PinSAGE for few-shot recommendations | Open-world recommendation · Amazon-Books few-shot users | AUC: +2.3% | Helped |
| Use IDCF-GC instead of NGCF for few-shot recommendations | Open-world recommendation · Amazon-Beauty few-shot users | NDCG: +1.0% | No clear effect |
| Use IDCF-GC instead of the best competitor | Open-world recommendation · Amazon-Books new users | AUC: 25.7% | Helped |
| Use IDCF-GC instead of the best competitor | Open-world recommendation · Amazon-Beauty new users | AUC: 22.8% | Helped |
| Train IDCF rather than a transductive CF model when ratings are scarce | Recommendation for users with very few ratings · ML-1M users with less than five historical ratings | RMSE: 2.5% | Helped |
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