<p align="center">
<img src="https://github.com/UlionTse/mlgb/blob/main/docs/mlgb_logo.png" width="200"/>
</p>
<p align="center">
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</p>
* * *
**MLGB** means **M**achine **L**earning of the **G**reat **B**oss, and is called **「妙计包」**.
**MLGB** is a library that includes many models of CTR Prediction & Recommender System by TensorFlow & PyTorch.
- [Advantages](#advantages)
- [Supported Models](#supported-models)
- [Installation](#installation)
- [Getting Started](#getting-started)
- [Code Examples](#code-examples)
- [Citation](#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](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/predictingclicks.pdf) | Microsoft | 2007 |
| 2 | PLM/MLR | [Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction](https://arxiv.org/pdf/1704.05194.pdf) | Alibaba | 2017 |
| 3 | MLP/DNN | [Neural Networks for Pattern Recognition](http://diyhpl.us/~bryan/papers2/ai/ahuman-pdf-only/neural-networks/2005-Pattern%20Recognition.pdf) | Christopher M. Bishop(Microsoft, 1997-Present), Foreword by Geoffrey Hinton. | 1995 |
| 4 | DLRM | [Deep Learning Recommendation Model for Personalization and Recommendation Systems](https://arxiv.org/pdf/1906.00091.pdf) | Facebook(Meta) | 2019 |
| 5 | MaskNet | [MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask](https://arxiv.org/pdf/2102.07619.pdf) | Weibo(Sina) | 2021 |
| | | | | |
| 6 | DCM/DeepCross | [Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features](https://www.kdd.org/kdd2016/papers/files/adf0975-shanA.pdf) | Microsoft | 2016 |
| 7 | DCN | [DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://arxiv.org/pdf/2008.13535.pdf), [v1](https://arxiv.org/pdf/1708.05123.pdf) | Google(Alphabet) | 2017, 2020 |
| 8 | EDCN | [Enhancing Explicit and Implicit Feature Interactions via Information Sharing for Parallel Deep CTR Models](https://dlp-kdd.github.io/assets/pdf/DLP-KDD_2021_paper_12.pdf) | Huawei | 2021 |
| | | | | |
| 9 | FM | [Factorization Machines](https://cseweb.ucsd.edu/classes/fa17/cse291-b/reading/Rendle2010FM.pdf) | Steffen Rendle(Google, 2013-Present) | 2010 |
| 10 | FFM | [Field-aware Factorization Machines for CTR Prediction](https://www.csie.ntu.edu.tw/~cjlin/papers/ffm.pdf) | NTU | 2016 |
| 11 | HOFM | [Higher-Order Factorization Machines](https://arxiv.org/pdf/1607.07195v2.pdf) | NTT | 2016 |
| 12 | FwFM | [Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising](https://arxiv.org/pdf/1806.03514.pdf) | Junwei Pan(Yahoo), etc. | 2018, 2020 |
| 13 | FmFM | [FM^2: Field-matrixed Factorization Machines for Recommender Systems](https://arxiv.org/pdf/2102.12994v2.pdf) | Yahoo | 2021 |
| 14 | FEFM | [FIELD-EMBEDDED FACTORIZATION MACHINES FOR CLICK-THROUGH RATE PREDICTION](https://arxiv.org/pdf/2009.09931v2.pdf) | Harshit Pande(Adobe) | 2020, 2021 |
| 15 | AFM | [Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks](https://arxiv.org/pdf/1708.04617.pdf) | ZJU&NUS(Jun Xiao(ZJU), Xiangnan He(NUS), etc.) | 2017 |
| 16 | LFM | [Learning Feature Interactions with Lorentzian Factorization Machine](https://arxiv.org/pdf/1911.09821.pdf) | EBay | 2019 |
| 17 | IFM | [An Input-aware Factorization Machine for Sparse Prediction](https://www.ijcai.org/proceedings/2019/0203.pdf) | THU | 2019 |
| 18 | DIFM | [A Dual Input-aware Factorization Machine for CTR Prediction](https://www.ijcai.org/proceedings/2020/0434.pdf) | THU | 2020 |
| | | | | |
| 19 | FNN | [Deep Learning over Multi-field Categorical Data – A Case Study on User Response Prediction](https://arxiv.org/pdf/1601.02376.pdf) | UCL(Weinan Zhang(UCL, SJTU), etc.) | 2016 |
| 20 | PNN | [Product-based Neural Networks for User Response](https://arxiv.org/pdf/1611.00144.pdf) | 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](https://arxiv.org/pdf/1807.00311.pdf) | Huawei(Yanru Qu(Huawei(2017.3-2018.3), SJTU), Weinan Zhang(SJTU, UCL), etc.) | 2018 |
| 22 | ONN/NFFM | [Operation-aware Neural Networks for User Response Prediction](https://arxiv.org/pdf/1904.12579.pdf) | NJU | 2019 |
| 23 | AFN | [Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions](https://arxiv.org/pdf/1909.03276v2.pdf) | SJTU | 2019, 2020 |
| | | | | |
| 24 | NFM | [Neural Factorization Machines for Sparse Predictive Analytics](https://arxiv.org/pdf/1708.05027.pdf) | NUS(Xiangnan He(NUS)) | 2017 |
| 25 | WDL | [Wide & Deep Learning for Recommender Systems](https://arxiv.org/pdf/1606.07792.pdf) | Google(Alphabet) | 2016 |
| 26 | DeepFM | [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://arxiv.org/pdf/1703.04247.pdf) | Huawei | 2017 |
| 27 | DeepFEFM | [FIELD-EMBEDDED FACTORIZATION MACHINES FOR CLICK-THROUGH RATE PREDICTION](https://arxiv.org/pdf/2009.09931v2.pdf) | Harshit Pande(Adobe) | 2020, 2021 |
| 28 | FLEN | [FLEN: Leveraging Field for Scalable CTR Prediction](https://arxiv.org/pdf/1911.04690v4.pdf) | Meitu | 2019, 2020 |
| | | | | |
| 29 | CCPM | [A Convolutional Click Prediction Model](http://wnzhang.net/share/rtb-papers/cnn-ctr.pdf) | CASIA | 2015 |
| 30 | FGCNN | [Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1904.04447.pdf) | Huawei | 2019 |
| 31 | XDeepFM | [xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://arxiv.org/pdf/1803.05170v3.pdf) | Microsoft(Jianxun Lian(USTC, Microsoft(2018.7-Present)), etc.) | 2018 |
| 32 | FiBiNet | [FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction](https://arxiv.org/pdf/1905.09433.pdf) | Weibo(Sina) | 2019 |
| 33 | AutoInt | [AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://arxiv.org/pdf/1810.11921v2.pdf) | PKU | 2018, 2019 |
| <tr><th colspan=5 align="center">:open_file_folder: **Ranking-Model::Sequential** :point_down:</th></tr> |
| 34 | GRU4Rec | [Session-based Recommendations with Recurrent Neural Networks](https://arxiv.org/pdf/1511.06939.pdf) | Telefonica | 2015, 2016 |
| 35 | Caser | [Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding](https://arxiv.org/pdf/1809.07426.pdf) | SFU | 2018 |
| 36 | SASRec | [Self-Attentive Sequential Recommendation](https://arxiv.org/pdf/1808.09781.pdf) | UCSD | 2018 |
| 37 | BERT4Rec | [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://arxiv.org/pdf/1904.06690.pdf) | Alibaba | 2019 |
| 38 | BST | [Behavior Sequence Transformer for E-commerce Recommendation in Alibaba](https://arxiv.org/pdf/1905.06874.pdf) | Alibaba | 2019 |
| 39 | DIN | [Deep Interest Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1706.06978v4.pdf), [v1](https://arxiv.org/pdf/1706.06978v1.pdf) | Alibaba | 2017, 2018 |
| 40 | DIEN | [Deep Interest Evolution Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1809.03672.pdf) | Alibaba | 2018 |
| 41 | DSIN | [Deep Session Interest Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1905.06482.pdf) | Alibaba | 2019 |
| <tr><th colspan=5 align="center">:open_file_folder: **Ranking-Model::Multitask** :point_down:</th></tr> |
| 42 | SharedBottom | [An Overview of Multi-Task Learning in Deep Neural Networks](https://arxiv.org/pdf/1706.05098.pdf) | Sebastian Ruder(InsightCentre) | 2017 |
| 43 | ESMM | [Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://arxiv.org/pdf/1804.07931.pdf) | Alibaba | 2018 |
| 44 | MMoE | [Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts](https://dl.acm.org/doi/pdf/10.1145/3219819.3220007) | Google(Alphabet) | 2018 |
| 45 | PLE | [Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations](https://www.sci-hub.se/10.1145/3383313.3412236) | Tencent | 2020 |
| 46 | PEPNet | [PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information](https://arxiv.org/pdf/2302.01115.pdf) | Kuaishou | 2023 |
| <tr><th colspan=5 align="center">:open_file_folder: **Matching-Model** :point_down:</th></tr> |
| 47 | NCF | [Neural Collaborative Filtering](https://arxiv.org/pdf/1708.05031.pdf) | NUS(Xiangnan He(NUS), etc) | 2017 |
| 48 | MatchFM | [Factorization Machines](https://cseweb.ucsd.edu/classes/fa17/cse291-b/reading/Rendle2010FM.pdf) | Steffen Rendle(Google, 2013-Present) | 2010 |
| 49 | DSSM | [Learning deep structured semantic models for web search using clickthrough data](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/cikm2013_DSSM_fullversion.pdf) | Microsoft | 2013 |
| 50 | EBR | [Embedding-based Retrieval in Facebook Search](https://browse.arxiv.org/pdf/2006.11632.pdf) | Facebook(Meta) | 2020 |
| 51 | YoutubeDNN | [Deep Neural Networks for YouTube Recommendations](https://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/45530.pdf) | Google(Alphabet) | 2016 |
| 52 | MIND | [Multi-Interest Network with Dynamic Routing for Recommendation at Tmall](https://arxiv.org/pdf/1904.08030.pdf) | Alibaba | 2019 |
| | | | | |
## Installation
```sh
# PYPI
pip install --upgrade mlgb
# Conda
conda install conda-forge::mlgb
```
## Getting Started
```python
import mlgb
# parameters of get_model:
help(mlgb.get_model)
"""
get_model(feature_names, model_name='LR', task='binary', aim='ranking', lang='TensorFlow', device=None, seed=None, **kwargs)
:param feature_names: tuple(tuple(dict)), must. Embedding need vocabulary size and custom embed_dim of features.
:param model_name: str, default 'LR'. Union[`mlgb.ranking_models`, `mlgb.matching_models`, `mlgb.mtl_models`]
:param task: str, default 'binary'. Union['binary', 'regression', 'multiclass:{int}']
:param aim: str, default 'ranking'. Union['ranking', 'matching', 'mtl']
:param lang: str, default 'TensorFlow'. Union['TensorFlow', 'PyTorch', 'tf', 'torch']
:param device: Optional[str, int], default None. Only for PyTorch.
:param seed: Optional[int], default None.
:param **kwargs: more model parameters by `mlgb.get_model_help(model_name)`.
"""
# parameters of model:
mlgb.get_model_help(model_name='LR', lang='tf')
"""
class LR(tf.keras.src.models.model.Model)
| LR(feature_names, task='binary', seed=None, inputs_if_multivalued=False, inputs_if_sequential=False, inputs_if_embed_dense=False, embed_dim=32, embed_2d_dim=None, embed_l2=0.0, embed_initializer=None, pool_mv_mode='Pooling:average', pool_mv_axis=2, pool_mv_l2=0.0, pool_mv_initializer=None, pool_seq_mode='Pooling:average', pool_seq_axis=1, pool_seq_l2=0.0, pool_seq_initializer=None, linear_if_bias=True, linear_l1=0.0, linear_l2=0.0, linear_initializer=None)
|
| Methods defined here:
|
| __init__(self, feature_names, task='binary', seed=None, inputs_if_multivalued=False, inputs_if_sequential=False, inputs_if_embed_dense=False, embed_dim=32, embed_2d_dim=None, embed_l2=0.0, embed_initializer=None, pool_mv_mode='Pooling:average', pool_mv_axis=2, pool_mv_l2=0.0, pool_mv_initializer=None, pool_seq_mode='Pooling:average', pool_seq_axis=1, pool_seq_l2=0.0, pool_seq_initializer=None, linear_if_bias=True, linear_l1=0.0, linear_l2=0.0, linear_initializer=None)
| Model Name: LR(LinearOrLogisticRegression)
| Paper Team: Microsoft
| Paper Year: 2007
| Paper Name: <Predicting Clicks: Estimating the Click-Through Rate for New Ads>
| Paper Link: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/predictingclicks.pdf
|
| Task Inputs Parameters:
| :param feature_names: tuple(tuple(dict)), must. Embedding need vocabulary size and custom embed_dim of features.
| :param task: str, default 'binary'. Union['binary', 'regression']
| :param seed: Optional[int], default None.
| :param inputs_if_multivalued: bool, default False.
| :param inputs_if_sequential: bool, default False.
| :param inputs_if_embed_dense: bool, default False.
| :param embed_dim: int, default 32.
| :param embed_2d_dim: Optional[int], default None. When None, each field has own embed_dim by feature_names.
| :param embed_l2: float, default 0.0.
| :param embed_initializer: Optional[str], default None. When None, activation judge first, xavier_normal end.
| :param pool_mv_mode: str, default 'Pooling:average'. Pooling mode of multivalued inputs. Union[
| 'Attention', 'Weighted', 'Pooling:max', 'Pooling:average', 'Pooling:sum']
| :param pool_mv_axis: int, default 2. Pooling axis of multivalued inputs.
| :param pool_mv_l2: float, default 0.0. When pool_mv_mode is in ('Weighted', 'Attention'), it works.
| :param pool_mv_initializer: Optional[str], default None. When None, activation judge first,
| xavier_normal end. When pool_mv_mode is in ('Weighted', 'Attention'), it works.
| :param pool_seq_mode: str, default 'Pooling:average'. Pooling mode of sequential inputs. Union[
| 'Attention', 'Weighted', 'Pooling:max', 'Pooling:average', 'Pooling:sum']
| :param pool_seq_axis: int, default 1. Pooling axis of sequential inputs.
| :param pool_seq_l2: float, default 0.0. When pool_seq_mode is in ('Weighted', 'Attention'), it works.
| :param pool_seq_initializer: Optional[str], default None. When None, activation judge first,
| xavier_normal end. When pool_seq_mode is in ('Weighted', 'Attention'), it works.
|
| Task Model Parameters:
| :param linear_if_bias: bool, default True.
| :param linear_l1: float, default 0.0.
| :param linear_l2: float, default 0.0.
| :param linear_initializer: Optional[str], default None. When None, activation judge first, xavier_normal end.
"""
```
## Code Examples
| Code Examples |
| ---------------------------------------------------------------------- |
| [TensorFlow](https://github.com/UlionTse/mlgb/tree/main/mlgb/examples) |
| [PyTorch](https://github.com/UlionTse/mlgb/tree/main/mlgb/examples) |
## Citation
If you use this for research, please cite it using the following BibTeX entry. Thanks.
```bibtex
@misc{uliontse2020mlgb,
author = {UlionTse},
title = {MLGB is a library that includes many models of CTR Prediction & Recommender System by TensorFlow & PyTorch},
year = {2020},
publisher = {GitHub},
journal = {GitHub Repository},
howpublished = {\url{https://github.com/UlionTse/mlgb}},
}
```
Raw data
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"maintainer_email": null,
"keywords": "Machine Learning, Deep Learning, CTR Prediction, Recommender System",
"author": "UlionTse",
"author_email": "uliontse@outlook.com",
"download_url": "https://files.pythonhosted.org/packages/0d/72/6074196f0bcc324ff8a5bdd41ed0e1a30ed84016a60c5e96b369534e076a/mlgb-0.0.6.tar.gz",
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"description": "<p align=\"center\">\r\n <img src=\"https://github.com/UlionTse/mlgb/blob/main/docs/mlgb_logo.png\" width=\"200\"/>\r\n</p>\r\n<p align=\"center\">\r\n <a href=\"https://pypi.org/project/mlgb\"><img alt=\"PyPI - Version\" src=\"https://img.shields.io/pypi/v/mlgb.svg?color=blue\"></a>\r\n <a href=\"https://anaconda.org/conda-forge/mlgb\"><img alt=\"Conda - Version\" src=\"https://img.shields.io/conda/vn/conda-forge/mlgb.svg?color=blue\"></a>\r\n <a href=\"https://pypi.org/project/mlgb\"><img alt=\"PyPI - License\" src=\"https://img.shields.io/pypi/l/mlgb.svg?color=brightgreen\"></a>\r\n <a href=\"https://pypi.org/project/mlgb\"><img alt=\"PyPI - Python\" src=\"https://img.shields.io/pypi/pyversions/mlgb.svg?color=blue\"></a>\r\n <a href=\"https://pypi.org/project/mlgb\"><img alt=\"PyPI - Status\" src=\"https://img.shields.io/pypi/status/mlgb.svg?color=brightgreen\"></a>\r\n <a href=\"https://pypi.org/project/mlgb\"><img alt=\"PyPI - Wheel\" src=\"https://img.shields.io/badge/wheel-yes-brightgreen.svg\"></a>\r\n <a href=\"https://pypi.org/project/mlgb\"><img alt=\"PyPI - Downloads\" src=\"https://static.pepy.tech/personalized-badge/mlgb?period=total&units=international_system&left_text=downloads&left_color=grey&right_color=blue\"></a>\r\n <a href=\"https://pypi.org/project/mlgb\"><img alt=\"PyPI - TensorFlow\" src=\"https://img.shields.io/badge/TensorFlow-2.10+-yellow.svg\"></a>\r\n <a href=\"https://pypi.org/project/mlgb\"><img alt=\"PyPI - PyTorch\" src=\"https://img.shields.io/badge/PyTorch-2.1+-tomato.svg\"></a>\r\n</p>\r\n\r\n* * *\r\n\r\n**MLGB** means **M**achine **L**earning of the **G**reat **B**oss, and is called **\u300c\u5999\u8ba1\u5305\u300d**. \r\n**MLGB** is a library that includes many models of CTR Prediction & Recommender System by TensorFlow & PyTorch.\r\n\r\n- [Advantages](#advantages)\r\n- [Supported Models](#supported-models)\r\n- [Installation](#installation)\r\n- [Getting Started](#getting-started)\r\n- [Code Examples](#code-examples)\r\n- [Citation](#citation)\r\n\r\n## Advantages\r\n\r\n- **Easy!** Use `mlgb.get_model(model_name, **kwargs)` to get a complex model.\r\n- **Fast!** Better performance through better code.\r\n- **Enjoyable!** 50+ ranking & matching models to use, 2 languages(TensorFlow & PyTorch) to deploy.\r\n\r\n## Supported Models\r\n\r\n| ID | Model Name | Paper Link | Paper Team | Paper Year |\r\n| --- | ------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------- | ---------- |\r\n| <tr><th colspan=5 align=\"center\">:open_file_folder: **Ranking-Model::Normal** :point_down:</th></tr> |\r\n| 1 | LR | [Predicting Clicks: Estimating the Click-Through Rate for New Ads](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/predictingclicks.pdf) | Microsoft | 2007 |\r\n| 2 | PLM/MLR | [Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction](https://arxiv.org/pdf/1704.05194.pdf) | Alibaba | 2017 |\r\n| 3 | MLP/DNN | [Neural Networks for Pattern Recognition](http://diyhpl.us/~bryan/papers2/ai/ahuman-pdf-only/neural-networks/2005-Pattern%20Recognition.pdf) | Christopher M. Bishop(Microsoft, 1997-Present), Foreword by Geoffrey Hinton. | 1995 |\r\n| 4 | DLRM | [Deep Learning Recommendation Model for Personalization and Recommendation Systems](https://arxiv.org/pdf/1906.00091.pdf) | Facebook(Meta) | 2019 |\r\n| 5 | MaskNet | [MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask](https://arxiv.org/pdf/2102.07619.pdf) | Weibo(Sina) | 2021 |\r\n| | | | | |\r\n| 6 | DCM/DeepCross | [Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features](https://www.kdd.org/kdd2016/papers/files/adf0975-shanA.pdf) | Microsoft | 2016 |\r\n| 7 | DCN | [DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://arxiv.org/pdf/2008.13535.pdf), [v1](https://arxiv.org/pdf/1708.05123.pdf) | Google(Alphabet) | 2017, 2020 |\r\n| 8 | EDCN | [Enhancing Explicit and Implicit Feature Interactions via Information Sharing for Parallel Deep CTR Models](https://dlp-kdd.github.io/assets/pdf/DLP-KDD_2021_paper_12.pdf) | Huawei | 2021 |\r\n| | | | | |\r\n| 9 | FM | [Factorization Machines](https://cseweb.ucsd.edu/classes/fa17/cse291-b/reading/Rendle2010FM.pdf) | Steffen Rendle(Google, 2013-Present) | 2010 |\r\n| 10 | FFM | [Field-aware Factorization Machines for CTR Prediction](https://www.csie.ntu.edu.tw/~cjlin/papers/ffm.pdf) | NTU | 2016 |\r\n| 11 | HOFM | [Higher-Order Factorization Machines](https://arxiv.org/pdf/1607.07195v2.pdf) | NTT | 2016 |\r\n| 12 | FwFM | [Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising](https://arxiv.org/pdf/1806.03514.pdf) | Junwei Pan(Yahoo), etc. | 2018, 2020 |\r\n| 13 | FmFM | [FM^2: Field-matrixed Factorization Machines for Recommender Systems](https://arxiv.org/pdf/2102.12994v2.pdf) | Yahoo | 2021 |\r\n| 14 | FEFM | [FIELD-EMBEDDED FACTORIZATION MACHINES FOR CLICK-THROUGH RATE PREDICTION](https://arxiv.org/pdf/2009.09931v2.pdf) | Harshit Pande(Adobe) | 2020, 2021 |\r\n| 15 | AFM | [Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks](https://arxiv.org/pdf/1708.04617.pdf) | ZJU&NUS(Jun Xiao(ZJU), Xiangnan He(NUS), etc.) | 2017 |\r\n| 16 | LFM | [Learning Feature Interactions with Lorentzian Factorization Machine](https://arxiv.org/pdf/1911.09821.pdf) | EBay | 2019 |\r\n| 17 | IFM | [An Input-aware Factorization Machine for Sparse Prediction](https://www.ijcai.org/proceedings/2019/0203.pdf) | THU | 2019 |\r\n| 18 | DIFM | [A Dual Input-aware Factorization Machine for CTR Prediction](https://www.ijcai.org/proceedings/2020/0434.pdf) | THU | 2020 |\r\n| | | | | |\r\n| 19 | FNN | [Deep Learning over Multi-field Categorical Data \u2013 A Case Study on User Response Prediction](https://arxiv.org/pdf/1601.02376.pdf) | UCL(Weinan Zhang(UCL, SJTU), etc.) | 2016 |\r\n| 20 | PNN | [Product-based Neural Networks for User Response](https://arxiv.org/pdf/1611.00144.pdf) | SJTU&UCL(Yanru Qu(SJTU), Weinan Zhang(SJTU, UCL), etc.) | 2016 |\r\n| 21 | PIN | [Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data](https://arxiv.org/pdf/1807.00311.pdf) | Huawei(Yanru Qu(Huawei(2017.3-2018.3), SJTU), Weinan Zhang(SJTU, UCL), etc.) | 2018 |\r\n| 22 | ONN/NFFM | [Operation-aware Neural Networks for User Response Prediction](https://arxiv.org/pdf/1904.12579.pdf) | NJU | 2019 |\r\n| 23 | AFN | [Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions](https://arxiv.org/pdf/1909.03276v2.pdf) | SJTU | 2019, 2020 |\r\n| | | | | |\r\n| 24 | NFM | [Neural Factorization Machines for Sparse Predictive Analytics](https://arxiv.org/pdf/1708.05027.pdf) | NUS(Xiangnan He(NUS)) | 2017 |\r\n| 25 | WDL | [Wide & Deep Learning for Recommender Systems](https://arxiv.org/pdf/1606.07792.pdf) | Google(Alphabet) | 2016 |\r\n| 26 | DeepFM | [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://arxiv.org/pdf/1703.04247.pdf) | Huawei | 2017 |\r\n| 27 | DeepFEFM | [FIELD-EMBEDDED FACTORIZATION MACHINES FOR CLICK-THROUGH RATE PREDICTION](https://arxiv.org/pdf/2009.09931v2.pdf) | Harshit Pande(Adobe) | 2020, 2021 |\r\n| 28 | FLEN | [FLEN: Leveraging Field for Scalable CTR Prediction](https://arxiv.org/pdf/1911.04690v4.pdf) | Meitu | 2019, 2020 |\r\n| | | | | |\r\n| 29 | CCPM | [A Convolutional Click Prediction Model](http://wnzhang.net/share/rtb-papers/cnn-ctr.pdf) | CASIA | 2015 |\r\n| 30 | FGCNN | [Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1904.04447.pdf) | Huawei | 2019 |\r\n| 31 | XDeepFM | [xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://arxiv.org/pdf/1803.05170v3.pdf) | Microsoft(Jianxun Lian(USTC, Microsoft(2018.7-Present)), etc.) | 2018 |\r\n| 32 | FiBiNet | [FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction](https://arxiv.org/pdf/1905.09433.pdf) | Weibo(Sina) | 2019 |\r\n| 33 | AutoInt | [AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://arxiv.org/pdf/1810.11921v2.pdf) | PKU | 2018, 2019 |\r\n| <tr><th colspan=5 align=\"center\">:open_file_folder: **Ranking-Model::Sequential** :point_down:</th></tr> |\r\n| 34 | GRU4Rec | [Session-based Recommendations with Recurrent Neural Networks](https://arxiv.org/pdf/1511.06939.pdf) | Telefonica | 2015, 2016 |\r\n| 35 | Caser | [Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding](https://arxiv.org/pdf/1809.07426.pdf) | SFU | 2018 |\r\n| 36 | SASRec | [Self-Attentive Sequential Recommendation](https://arxiv.org/pdf/1808.09781.pdf) | UCSD | 2018 |\r\n| 37 | BERT4Rec | [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://arxiv.org/pdf/1904.06690.pdf) | Alibaba | 2019 |\r\n| 38 | BST | [Behavior Sequence Transformer for E-commerce Recommendation in Alibaba](https://arxiv.org/pdf/1905.06874.pdf) | Alibaba | 2019 |\r\n| 39 | DIN | [Deep Interest Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1706.06978v4.pdf), [v1](https://arxiv.org/pdf/1706.06978v1.pdf) | Alibaba | 2017, 2018 |\r\n| 40 | DIEN | [Deep Interest Evolution Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1809.03672.pdf) | Alibaba | 2018 |\r\n| 41 | DSIN | [Deep Session Interest Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1905.06482.pdf) | Alibaba | 2019 |\r\n| <tr><th colspan=5 align=\"center\">:open_file_folder: **Ranking-Model::Multitask** :point_down:</th></tr> |\r\n| 42 | SharedBottom | [An Overview of Multi-Task Learning in Deep Neural Networks](https://arxiv.org/pdf/1706.05098.pdf) | Sebastian Ruder(InsightCentre) | 2017 |\r\n| 43 | ESMM | [Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://arxiv.org/pdf/1804.07931.pdf) | Alibaba | 2018 |\r\n| 44 | MMoE | [Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts](https://dl.acm.org/doi/pdf/10.1145/3219819.3220007) | Google(Alphabet) | 2018 |\r\n| 45 | PLE | [Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations](https://www.sci-hub.se/10.1145/3383313.3412236) | Tencent | 2020 |\r\n| 46 | PEPNet | [PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information](https://arxiv.org/pdf/2302.01115.pdf) | Kuaishou | 2023 |\r\n| <tr><th colspan=5 align=\"center\">:open_file_folder: **Matching-Model** :point_down:</th></tr> |\r\n| 47 | NCF | [Neural Collaborative Filtering](https://arxiv.org/pdf/1708.05031.pdf) | NUS(Xiangnan He(NUS), etc) | 2017 |\r\n| 48 | MatchFM | [Factorization Machines](https://cseweb.ucsd.edu/classes/fa17/cse291-b/reading/Rendle2010FM.pdf) | Steffen Rendle(Google, 2013-Present) | 2010 |\r\n| 49 | DSSM | [Learning deep structured semantic models for web search using clickthrough data](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/cikm2013_DSSM_fullversion.pdf) | Microsoft | 2013 |\r\n| 50 | EBR | [Embedding-based Retrieval in Facebook Search](https://browse.arxiv.org/pdf/2006.11632.pdf) | Facebook(Meta) | 2020 |\r\n| 51 | YoutubeDNN | [Deep Neural Networks for YouTube Recommendations](https://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/45530.pdf) | Google(Alphabet) | 2016 |\r\n| 52 | MIND | [Multi-Interest Network with Dynamic Routing for Recommendation at Tmall](https://arxiv.org/pdf/1904.08030.pdf) | Alibaba | 2019 |\r\n| | | | | |\r\n\r\n## Installation\r\n\r\n```sh\r\n# PYPI\r\npip install --upgrade mlgb\r\n\r\n# Conda\r\nconda install conda-forge::mlgb\r\n```\r\n\r\n## Getting Started\r\n\r\n```python\r\nimport mlgb\r\n\r\n# parameters of get_model:\r\nhelp(mlgb.get_model)\r\n\r\n\"\"\"\r\nget_model(feature_names, model_name='LR', task='binary', aim='ranking', lang='TensorFlow', device=None, seed=None, **kwargs)\r\n :param feature_names: tuple(tuple(dict)), must. Embedding need vocabulary size and custom embed_dim of features.\r\n :param model_name: str, default 'LR'. Union[`mlgb.ranking_models`, `mlgb.matching_models`, `mlgb.mtl_models`]\r\n :param task: str, default 'binary'. Union['binary', 'regression', 'multiclass:{int}']\r\n :param aim: str, default 'ranking'. Union['ranking', 'matching', 'mtl']\r\n :param lang: str, default 'TensorFlow'. Union['TensorFlow', 'PyTorch', 'tf', 'torch']\r\n :param device: Optional[str, int], default None. Only for PyTorch.\r\n :param seed: Optional[int], default None.\r\n :param **kwargs: more model parameters by `mlgb.get_model_help(model_name)`.\r\n\"\"\"\r\n\r\n# parameters of model:\r\nmlgb.get_model_help(model_name='LR', lang='tf')\r\n\r\n\"\"\"\r\n class LR(tf.keras.src.models.model.Model)\r\n | LR(feature_names, task='binary', seed=None, inputs_if_multivalued=False, inputs_if_sequential=False, inputs_if_embed_dense=False, embed_dim=32, embed_2d_dim=None, embed_l2=0.0, embed_initializer=None, pool_mv_mode='Pooling:average', pool_mv_axis=2, pool_mv_l2=0.0, pool_mv_initializer=None, pool_seq_mode='Pooling:average', pool_seq_axis=1, pool_seq_l2=0.0, pool_seq_initializer=None, linear_if_bias=True, linear_l1=0.0, linear_l2=0.0, linear_initializer=None)\r\n | \r\n | Methods defined here:\r\n | \r\n | __init__(self, feature_names, task='binary', seed=None, inputs_if_multivalued=False, inputs_if_sequential=False, inputs_if_embed_dense=False, embed_dim=32, embed_2d_dim=None, embed_l2=0.0, embed_initializer=None, pool_mv_mode='Pooling:average', pool_mv_axis=2, pool_mv_l2=0.0, pool_mv_initializer=None, pool_seq_mode='Pooling:average', pool_seq_axis=1, pool_seq_l2=0.0, pool_seq_initializer=None, linear_if_bias=True, linear_l1=0.0, linear_l2=0.0, linear_initializer=None)\r\n | Model Name: LR(LinearOrLogisticRegression)\r\n | Paper Team: Microsoft\r\n | Paper Year: 2007\r\n | Paper Name: <Predicting Clicks: Estimating the Click-Through Rate for New Ads>\r\n | Paper Link: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/predictingclicks.pdf\r\n | \r\n | Task Inputs Parameters:\r\n | :param feature_names: tuple(tuple(dict)), must. Embedding need vocabulary size and custom embed_dim of features.\r\n | :param task: str, default 'binary'. Union['binary', 'regression']\r\n | :param seed: Optional[int], default None.\r\n | :param inputs_if_multivalued: bool, default False.\r\n | :param inputs_if_sequential: bool, default False.\r\n | :param inputs_if_embed_dense: bool, default False.\r\n | :param embed_dim: int, default 32.\r\n | :param embed_2d_dim: Optional[int], default None. When None, each field has own embed_dim by feature_names.\r\n | :param embed_l2: float, default 0.0.\r\n | :param embed_initializer: Optional[str], default None. When None, activation judge first, xavier_normal end.\r\n | :param pool_mv_mode: str, default 'Pooling:average'. Pooling mode of multivalued inputs. Union[\r\n | 'Attention', 'Weighted', 'Pooling:max', 'Pooling:average', 'Pooling:sum']\r\n | :param pool_mv_axis: int, default 2. Pooling axis of multivalued inputs.\r\n | :param pool_mv_l2: float, default 0.0. When pool_mv_mode is in ('Weighted', 'Attention'), it works.\r\n | :param pool_mv_initializer: Optional[str], default None. When None, activation judge first,\r\n | xavier_normal end. When pool_mv_mode is in ('Weighted', 'Attention'), it works.\r\n | :param pool_seq_mode: str, default 'Pooling:average'. Pooling mode of sequential inputs. Union[\r\n | 'Attention', 'Weighted', 'Pooling:max', 'Pooling:average', 'Pooling:sum']\r\n | :param pool_seq_axis: int, default 1. Pooling axis of sequential inputs.\r\n | :param pool_seq_l2: float, default 0.0. When pool_seq_mode is in ('Weighted', 'Attention'), it works.\r\n | :param pool_seq_initializer: Optional[str], default None. When None, activation judge first,\r\n | xavier_normal end. When pool_seq_mode is in ('Weighted', 'Attention'), it works.\r\n | \r\n | Task Model Parameters:\r\n | :param linear_if_bias: bool, default True.\r\n | :param linear_l1: float, default 0.0.\r\n | :param linear_l2: float, default 0.0.\r\n | :param linear_initializer: Optional[str], default None. When None, activation judge first, xavier_normal end.\r\n\"\"\"\r\n```\r\n\r\n## Code Examples\r\n\r\n| Code Examples |\r\n| ---------------------------------------------------------------------- |\r\n| [TensorFlow](https://github.com/UlionTse/mlgb/tree/main/mlgb/examples) |\r\n| [PyTorch](https://github.com/UlionTse/mlgb/tree/main/mlgb/examples) |\r\n\r\n## Citation\r\n\r\nIf you use this for research, please cite it using the following BibTeX entry. Thanks.\r\n\r\n```bibtex\r\n@misc{uliontse2020mlgb,\r\n author = {UlionTse},\r\n title = {MLGB is a library that includes many models of CTR Prediction & Recommender System by TensorFlow & PyTorch},\r\n year = {2020},\r\n publisher = {GitHub},\r\n journal = {GitHub Repository},\r\n howpublished = {\\url{https://github.com/UlionTse/mlgb}},\r\n}\r\n```\r\n",
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