| Logistic Regression, LR |
| Mixed Logistic Regression, MLR |
Kun Gai, et al. Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction, 2017. |
| GBDT + LR |
| Factorization Machine, FM |
S Rendle, Factorization Machines, 2010. |
| Field-aware Factorization Machine, FFM |
Y Juan, et al. Field-aware Factorization Machines for CTR Prediction, 2015. |
| Deep Crossing |
Ying Shan, et al.Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features, 2016. |
| Product-based Neural Network, PNN |
Y Qu, et al. Product-based Neural Networks for User Response Prediction, 2016. |
| Wide & Deep |
HT Cheng, et al. Wide & Deep Learning for Recommender Systems, 2016. |
| Deep & Cross Network, DCN |
R Wang, et al. Deep & Cross Network for Ad Click Predictions, 2017. |
| Factorization Machine supported Neural Network, FNN |
W Zhang, et al. Deep Learning over Multi-field Categorical Data - A Case Study on User Response Prediction, 2016. |
| DeepFM |
H Guo, et al. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction, 2017. |
| Neural Factorization Machine, NFM |
X He and TS Chua, Neural Factorization Machines for Sparse Predictive Analytics, 2017. |
| Attentional Factorization Machine, AFM |
J Xiao, et al. Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks, 2017. |
| Deep Interest Network, DIN |
Guorui Zhou, et al. Deep Interest Network for Click-Through Rate Prediction, 2017. |
| Deep Interest Evolution Network, DIEN |
Guorui Zhou, et al. Deep Interest Evolution Network for Click-Through Rate Prediction, 2018. |
| Latent Factor Model, LFM |
| Neural Collaborative Filtering, NeuralCF |
X He, et al. Neural Collaborative Filtering, 2017. |