| Convolutional Click Prediction Model | [CIKM 2015]A Convolutional Click Prediction Model |
| Factorization-supported Neural Network | [ECIR 2016]Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction |
| Product-based Neural Network | [ICDM 2016]Product-based neural networks for user response prediction |
| Wide & Deep | [DLRS 2016]Wide & Deep Learning for Recommender Systems |
| DeepFM | [IJCAI 2017]DeepFM: A Factorization-Machine based Neural Network for CTR Prediction |
| Piece-wise Linear Model | [arxiv 2017]Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction |
| Deep & Cross Network | [ADKDD 2017]Deep & Cross Network for Ad Click Predictions |
| Attentional Factorization Machine | [IJCAI 2017]Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks |
| Neural Factorization Machine | [SIGIR 2017]Neural Factorization Machines for Sparse Predictive Analytics |
| xDeepFM | [KDD 2018]xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems |
| Deep Interest Network | [KDD 2018]Deep Interest Network for Click-Through Rate Prediction |
| Deep Interest Evolution Network | [AAAI 2019]Deep Interest Evolution Network for Click-Through Rate Prediction |
| AutoInt | [CIKM 2019]AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks |
| ONN | [arxiv 2019]Operation-aware Neural Networks for User Response Prediction |
| FiBiNET | [RecSys 2019]FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction |
| IFM | [IJCAI 2019]An Input-aware Factorization Machine for Sparse Prediction |
| DCN V2 | [arxiv 2020]DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems |
| DIFM | [IJCAI 2020]A Dual Input-aware Factorization Machine for CTR Prediction |
| AFN | [AAAI 2020]Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions |
| SharedBottom | [arxiv 2017]An Overview of Multi-Task Learning in Deep Neural Networks |
| ESMM | [SIGIR 2018]Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate |
| MMOE | [KDD 2018]Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts |
| PLE | [RecSys 2020]Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations |
