Click Through Rate Prediction On Ipinyou
Metriken
AUC
LogLoss
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Modellname | AUC | LogLoss | Paper Title | Repository |
|---|---|---|---|---|
| OptInter | 0.7825 | 0.005604 | Memorize, Factorize, or be Naïve: Learning Optimal Feature Interaction Methods for CTR Prediction | - |
| PNN* | 0.7661 | - | Product-based Neural Networks for User Response Prediction | - |
| FNN | 0.7619 | - | Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction | - |
| IPNN | 0.7914 | - | Product-based Neural Networks for User Response Prediction | - |
| OPNN | 0.8174 | - | Product-based Neural Networks for User Response Prediction | - |
| DCNv3 | 0.7856 | 0.005535 | FCN: Fusing Exponential and Linear Cross Network for Click-Through Rate Prediction | - |
| OptInter-M | 0.7800 | 0.00564 | Memorize, Factorize, or be Naïve: Learning Optimal Feature Interaction Methods for CTR Prediction | - |
0 of 7 row(s) selected.