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MLGB means Machine Learning of the Great Boss, and is called 「妙计包」.
MLGB is a library that includes many models of CTR Prediction & Recommender System by TensorFlow & PyTorch.
Advantages
- Easy! Use
mlgb.get_model(model_name, **kwargs) to get a complex model.
- Fast! Better performance through better code.
- Enjoyable! 50+ ranking & matching models to use, 2 languages(TensorFlow & PyTorch) to deploy.
Supported Models
| ID | Model Name | Paper Link | Paper Team | Paper Year |
|---|
| <tr><th colspan=5 align="center">:open_file_folder: Ranking-Model::Normal :point_down:</th></tr> | | | | |
| 1 | LR | Predicting Clicks: Estimating the Click-Through Rate for New Ads | Microsoft | 2007 |
| 2 | PLM/MLR | Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction | Alibaba | 2017 |
| 3 | MLP/DNN | Neural Networks for Pattern Recognition | Christopher M. Bishop(Microsoft, 1997-Present), Foreword by Geoffrey Hinton. | 1995 |
| 4 | DLRM | Deep Learning Recommendation Model for Personalization and Recommendation Systems | Facebook(Meta) | 2019 |
| 5 | MaskNet | MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask | Weibo(Sina) | 2021 |
| | | | |
| 6 | DCM/DeepCross | Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features | Microsoft | 2016 |
| 7 | DCN | DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems, v1 | Google(Alphabet) | 2017, 2020 |
| 8 | EDCN | Enhancing Explicit and Implicit Feature Interactions via Information Sharing for Parallel Deep CTR Models | Huawei | 2021 |
| | | | |
| 9 | FM | Factorization Machines | Steffen Rendle(Google, 2013-Present) | 2010 |
| 10 | FFM | Field-aware Factorization Machines for CTR Prediction | NTU | 2016 |
| 11 | HOFM | Higher-Order Factorization Machines | NTT | 2016 |
| 12 | FwFM | Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising | Junwei Pan(Yahoo), etc. | 2018, 2020 |
| 13 | FmFM | FM^2: Field-matrixed Factorization Machines for Recommender Systems | Yahoo | 2021 |
| 14 | FEFM | FIELD-EMBEDDED FACTORIZATION MACHINES FOR CLICK-THROUGH RATE PREDICTION | Harshit Pande(Adobe) | 2020, 2021 |
| 15 | AFM | Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks | ZJU&NUS(Jun Xiao(ZJU), Xiangnan He(NUS), etc.) | 2017 |
| 16 | LFM | Learning Feature Interactions with Lorentzian Factorization Machine | EBay | 2019 |
| 17 | IFM | An Input-aware Factorization Machine for Sparse Prediction | THU | 2019 |
| 18 | DIFM | A Dual Input-aware Factorization Machine for CTR Prediction | THU | 2020 |
| | | | |
| 19 | FNN | Deep Learning over Multi-field Categorical Data – A Case Study on User Response Prediction | UCL(Weinan Zhang(UCL, SJTU), etc.) | 2016 |
| 20 | PNN | Product-based Neural Networks for User Response | SJTU&UCL(Yanru Qu(SJTU), Weinan Zhang(SJTU, UCL), etc.) | 2016 |
| 21 | PIN | Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data | Huawei(Yanru | |
