Click Through Rate Prediction On Ipinyou
평가 지표
AUC
LogLoss
평가 결과
이 벤치마크에서 각 모델의 성능 결과
모델 이름 | 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 | - |
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