Click Through Rate Prediction On Bing News
평가 지표
AUC
Log Loss
평가 결과
이 벤치마크에서 각 모델의 성능 결과
모델 이름 | AUC | Log Loss | Paper Title | Repository |
|---|---|---|---|---|
| PNN | 0.8321 | 0.2775 | Product-based Neural Networks for User Response Prediction | |
| DKN | 0.659 | - | DKN: Deep Knowledge-Aware Network for News Recommendation | |
| DNN | 0.03 | 0.3382 | xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems | |
| RippleNet | 0.678 | - | RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems | |
| DeepFM | 0.8376 | 0.2671 | DeepFM: A Factorization-Machine based Neural Network for CTR Prediction | |
| Wide & Deep | 0.8377 | 0.2668 | Wide & Deep Learning for Recommender Systems | |
| xDeepFM | 0.84 | 0.2649 | xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems |
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