| Factorization-supported Neural Network (FNN) | [ECIR 2016]Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction |
| Product-based Neural Network (PNN) | [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 |
| Deep & Cross Network (DCN) | [ADKDD 2017]Deep & Cross Network for Ad Click Predictions |
| xDeepFM | [KDD 2018]xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems |
| Neural Factorization Machine (NFM) | [SIGIR 2017]Neural Factorization Machines for Sparse Predictive Analytics |
| Attentional Factorization Machine (AFM) | [IJCAI 2017]Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks |
| AutoInt | [arxiv 2018]AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks |
| Convolutional Click Prediction Model (CCPM) | [CIKM 2015]A Convolutional Click Prediction Model |
| Feature Generation by Convolutional Neural Network (FGCNN) | [WWW 2019]Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction |
| Mixed Logistic Regression (MLR) | [arxiv 2017]Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction |
| FiBiNET | [RecSys 2019]FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction |
| NFFM | [arxiv 2019]Operation-aware Neural Networks for User Response Prediction |