https://github.com/uliontse/mlgb
MLGB is a library that includes many models of CTR Prediction & Recommender System by TensorFlow & PyTorch. 「妙计包」是一个包含50+点击率预估和推荐系统深度模型的、通过TensorFlow和PyTorch撰写的库。
https://github.com/uliontse/mlgb
autoint ctr-prediction dcn deep-learning deepfm din dsin dssm edcn esmm fibinet machine-learning masknet mind mmoe pepnet ple pnn recommender-system xdeepfm
Last synced: 4 days ago
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MLGB is a library that includes many models of CTR Prediction & Recommender System by TensorFlow & PyTorch. 「妙计包」是一个包含50+点击率预估和推荐系统深度模型的、通过TensorFlow和PyTorch撰写的库。
- Host: GitHub
- URL: https://github.com/uliontse/mlgb
- Owner: UlionTse
- License: apache-2.0
- Created: 2024-01-02T18:18:10.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-02T02:41:11.000Z (3 months ago)
- Last Synced: 2025-05-12T12:07:18.944Z (6 days ago)
- Topics: autoint, ctr-prediction, dcn, deep-learning, deepfm, din, dsin, dssm, edcn, esmm, fibinet, machine-learning, masknet, mind, mmoe, pepnet, ple, pnn, recommender-system, xdeepfm
- Language: Python
- Homepage:
- Size: 548 KB
- Stars: 689
- Watchers: 8
- Forks: 22
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: change_log.txt
- License: LICENSE
Awesome Lists containing this project
README
* * *
**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 |
| --- | ------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------- | ---------- |
| :open_file_folder: **Ranking-Model::Normal** :point_down: |
| 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 |
| :open_file_folder: **Ranking-Model::Sequential** :point_down: |
| 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 |
| :open_file_folder: **Ranking-Model::Multitask** :point_down: |
| 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 |
| :open_file_folder: **Matching-Model** :point_down: |
| 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:
| 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}},
}
```