mlgb

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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
• 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.

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