Click Through Rate Prediction On Movielens 1M
Metrics
AUC
Accuracy
Results
Performance results of various models on this benchmark
Model Name | AUC | Accuracy | Paper Title | Repository |
|---|---|---|---|---|
| RippleNet | 0.921 | 84.4 | RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems | - |
| AutoInt | 0.846 | - | AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks | - |
| MKR | 0.917 | 84.3 | Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation | - |
| KNI | 0.9449 | - | An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced Recommendation | - |
| STEC | 0.9712 | - | STEC: See-Through Transformer-based Encoder for CTR Prediction | - |
| DCNv3 | 0.9074 | - | FCN: Fusing Exponential and Linear Cross Network for Click-Through Rate Prediction | - |
0 of 6 row(s) selected.