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
Supported Models
Installation
Getting Started
Code Examples
Citation

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.
PaperWithCode

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

Mlgb

Install / Use

README

Advantages

Supported Models