| Revisiting Feature Interactions from the Perspective of Quadratic Neural Networks for Click-through Rate Prediction | ✓ Link | 0.8163 | 0.4358 | QNN-α | 2025-05-23 |
| FCN: Fusing Exponential and Linear Cross Network for Click-Through Rate Prediction | ✓ Link | 0.8162 | 0.4358 | FCN | 2024-07-18 |
| Towards Deeper, Lighter and Interpretable Cross Network for CTR Prediction | ✓ Link | 0.8161 | 0.4360 | GDCN | 2023-11-08 |
| MemoNet: Memorizing All Cross Features' Representations Efficiently via Multi-Hash Codebook Network for CTR Prediction | ✓ Link | 0.8152 | | MemoNet | 2022-10-25 |
| TF4CTR: Twin Focus Framework for CTR Prediction via Adaptive Sample Differentiation | ✓ Link | 0.8150 | | TF4CTR | 2024-05-06 |
| MMBAttn: Max-Mean and Bit-wise Attention for CTR Prediction | | 0.81497 | | FinalMLP + MMBAttn | 2023-08-25 |
| FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction | ✓ Link | 0.8149 | | FinalMLP | 2023-04-03 |
| CETN: Contrast-enhanced Through Network for CTR Prediction | ✓ Link | 0.8148 | 0.4373 | CETN | 2023-12-15 |
| STEC: See-Through Transformer-based Encoder for CTR Prediction | | 0.8143 | 0.4379 | STEC | 2023-08-29 |
| MMBAttn: Max-Mean and Bit-wise Attention for CTR Prediction | | 0.8143 | | DNN + MMBAttn | 2023-08-25 |
| MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask | ✓ Link | 0.8131 | | MaskNet | 2021-02-09 |
| DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Serving | ✓ Link | 0.8123 | 0.4395 | DeepLight | 2020-02-17 |
| Cognitive Evolutionary Search to Select Feature Interactions for Click-Through Rate Prediction | ✓ Link | 0.8117 | 0.4400 | CELS | 2023-08-01 |
| Optimizing Feature Set for Click-Through Rate Prediction | ✓ Link | 0.8116 | 0.4401 | OptFS | 2023-01-26 |
| DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems | ✓ Link | 0.8115 | 0.4406 | DCN V2 | 2020-08-19 |
| OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction | ✓ Link | 0.8114 | 0.44 | OptEmbed | 2022-08-09 |
| ContextNet: A Click-Through Rate Prediction Framework Using Contextual information to Refine Feature Embedding | ✓ Link | 0.8113 | | ContextNet | 2021-07-26 |
| FiBiNet++: Reducing Model Size by Low Rank Feature Interaction Layer for CTR Prediction | ✓ Link | 0.8110 | | FiBiNet++ | 2022-09-12 |
| Correct Normalization Matters: Understanding the Effect of Normalization On Deep Neural Network Models For Click-Through Rate Prediction | ✓ Link | 0.8107 | | NormDNN | 2020-06-23 |
| FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine | ✓ Link | 0.8104 | 0.4416 | DeepFFM | 2019-05-15 |
| FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction | ✓ Link | 0.8103 | 0.4423 | FiBiNET | 2019-05-23 |
| Memorize, Factorize, or be Naïve: Learning Optimal Feature Interaction Methods for CTR Prediction | ✓ Link | 0.8101 | 0.4417 | OptInter | 2021-08-03 |
| GateNet: Gating-Enhanced Deep Network for Click-Through Rate Prediction | ✓ Link | 0.8100 | | GateNet | 2020-07-06 |
| Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions | ✓ Link | 0.8074 | | AFN+ | 2019-09-07 |
| XCrossNet: Feature Structure-Oriented Learning for Click-Through Rate Prediction | ✓ Link | 0.8067 | | XCrossNet | 2021-04-22 |
| Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction | ✓ Link | 0.8062 | 0.4453 | Fi-GNN | 2019-10-12 |
| AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks | ✓ Link | 0.8061 | 0.4454 | AutoInt | 2018-10-29 |
| Clustering the Sketch: A Novel Approach to Embedding Table Compression | ✓ Link | 0.806 | 0.449 | Clustered Compositional Embeddings | 2022-10-12 |
| xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems | ✓ Link | 0.8052 | 0.4418 | xDeepFM | 2018-03-14 |
| Weighted Multi-Level Feature Factorization for App ads CTR and installation prediction | ✓ Link | 0.804 | 0.447 | WMLFF | 2023-08-03 |
| Feature Interaction based Neural Network for Click-Through Rate Prediction | | 0.8020 | 0.5409 | FINN | 2020-06-07 |
| AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction | ✓ Link | 0.8010 | 0.5405 | AutoDeepFM(3rd) | 2020-03-25 |
| DeepFM: A Factorization-Machine based Neural Network for CTR Prediction | ✓ Link | 0.8007 | 0.45083 | DeepFM | 2017-03-13 |
| TFNet: Multi-Semantic Feature Interaction for CTR Prediction | | 0.7991 | | TFNet | 2020-06-29 |
| Product-based Neural Networks for User Response Prediction | ✓ Link | 0.7987 | 0.45214 | PNN* | 2016-11-01 |
| Product-based Neural Networks for User Response Prediction | ✓ Link | 0.7982 | 0.45256 | OPNN | 2016-11-01 |
| Wide & Deep Learning for Recommender Systems | ✓ Link | 0.7981 | 0.46772 | Wide&Deep | 2016-06-24 |
| Product-based Neural Networks for User Response Prediction | ✓ Link | 0.7972 | 0.45323 | IPNN | 2016-11-01 |
| Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction | ✓ Link | 0.7963 | 0.45738 | FNN | 2016-01-11 |