UlionTse/mlgb — Star 增长与排名

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。

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

:open_file_folder: Ranking-Model::Normal :point_down:

:open_file_folder: Ranking-Model::Sequential :point_down:

| | 34 | GRU4Rec | Session-based Recommendations with Recurrent Neural Networks | Telefonica | 2015, 2016 | | 35 | Caser | Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding | SFU | 2018 | | 36 | SASRec | Self-Attentive Sequential Recommendation | UCSD | 2018 | | 37 | BERT4Rec | BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer | Alibaba | 2019 | | 38 | BST | Behavior Sequence Transformer for E-commerce Recommendation in Alibaba | Alibaba | 2019 | | 39 | DIN | Deep Interest Network for Click-Through Rate Prediction, v1 | Alibaba | 2017, 2018 | | 40 | DIEN | Deep Interest Evolution Network for Click-Through Rate Prediction | Alibaba | 2018 | | 41 | DSIN | Deep Session Interest Network for Click-Through Rate Prediction | Alibaba | 2019 | |

:open_file_folder: Ranking-Model::Multitask :point_down:

| | 42 | SharedBottom | An Overview of Multi-Task Learning in Deep Neural Networks | Sebastian Ruder(InsightCentre) | 2017 | | 43 | ESMM | Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate | Alibaba | 2018 | | 44 | MMoE | Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts | Google(Alphabet) | 2018 | | 45 | PLE | Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations | Tencent | 2020 | | 46 | PEPNet | PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information | Kuaishou | 2023 | |

:open_file_folder: Matching-Model :point_down:

| | 47 | NCF | Neural Collaborative Filtering | NUS(Xiangnan He(NUS), etc) | 2017 | | 48 | MatchFM | Factorization Machines | Steffen Rendle(Google, 2013-Present) | 2010 | | 49 | DSSM | Learning deep structured semantic models for web search using clickthrough data | Microsoft | 2013 | | 50 | EBR | Embedding-based Retrieval in Facebook Search | Facebook(Meta) | 2020 | | 51 | YoutubeDNN | Deep Neural Networks for YouTube Recommendations | Google(Alphabet) | 2016 | | 52 | MIND | Multi-Interest Network with Dynamic Routing for Recommendation at Tmall | Alibaba | 2019 | | | | | | |

Installation

# PYPI
pip install --upgrade mlgb

# Conda
conda install conda-forge::mlgb

Getting Started

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
PyTorch

Citation

If you use this for research, please cite it using the following BibTeX entry. Thanks.

@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}},
}