开源软件名称：

PaddleRec

开源软件地址：

https://gitee.com/paddlepaddle/PaddleRec

开源软件介绍：

(中文文档|简体中文|English)

注意：定期从github同步至该仓库，更多模型请参考https://github.com/PaddlePaddle/PaddleRec

什么是推荐系统?

推荐系统是在互联网信息爆炸式增长的时代背景下，帮助用户高效获得感兴趣信息的关键；
推荐系统也是帮助产品最大限度吸引用户、留存用户、增加用户粘性、提高用户转化率的银弹。
有无数优秀的产品依靠用户可感知的推荐系统建立了良好的口碑，也有无数的公司依靠直击用户痛点的推荐系统在行业中占领了一席之地。
可以说，谁能掌握和利用好推荐系统，谁就能在信息分发的激烈竞争中抢得先机。但与此同时，有着许多问题困扰着推荐系统的开发者，比如：庞大的数据量，复杂的模型结构，低效的分布式训练环境，波动的在离线一致性，苛刻的上线部署要求，以上种种，不胜枚举。

什么是PaddleRec?

源于飞桨生态的搜索推荐模型 一站式开箱即用工具
适合初学者，开发者，研究者的推荐系统全流程解决方案
包含内容理解、匹配、召回、排序、多任务、重排序等多个任务的完整推荐搜索算法库

快速使用

环境要求

Python 2.7.15 / 3.5 / 3.6 / 3.7, 推荐使用python3.7，示例中的python默认表示python3.7
PaddlePaddle >=2.0
操作系统: Windows/Mac/Linux
Windows下PaddleRec目前仅支持单机训练，分布式训练建议使用Linux环境

安装Paddle

gpu环境pip安装

python -m pip install paddlepaddle-gpu==2.0.0

cpu环境pip安装

python -m pip install paddlepaddle # gcc8

更多版本下载可参考paddle官网下载安装

下载PaddleRec

git clone https://github.com/PaddlePaddle/PaddleRec/cd PaddleRec

快速运行

我们以排序模型中的dnn模型为例介绍PaddleRec的一键启动。训练数据来源为Criteo数据集，我们从中截取了100条数据：

python -u tools/trainer.py -m models/rank/dnn/config.yaml # 动态图训练 python -u tools/static_trainer.py -m models/rank/dnn/config.yaml #  静态图训练

帮助文档

项目背景

入门教程

进阶教程

FAQ

常见问题FAQ

支持模型列表

方向	模型	单机CPU	单机GPU	分布式CPU	分布式GPU	支持版本	论文
内容理解	TextCnn	✓	✓	✓	x	>=2.1.0	[EMNLP 2014]Convolutional neural networks for sentence classication
内容理解	TagSpace	✓	✓	✓	x	>=2.1.0	[EMNLP 2014]TagSpace: Semantic Embeddings from Hashtags
匹配	DSSM	✓	✓	✓	x	>=2.1.0	[CIKM 2013]Learning Deep Structured Semantic Models for Web Search using Clickthrough Data
匹配	MultiView-Simnet	✓	✓	✓	x	>=2.1.0	[WWW 2015]A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems
召回	TDM	✓	>=1.8.0	✓	>=1.8.0	1.8.5	[KDD 2018]Learning Tree-based Deep Model for Recommender Systems
召回	fasttext	✓	✓	x	x	1.8.5	[EACL 2017]Bag of Tricks for Efficient Text Classification
召回	MIND	✓	✓	x	x	>=2.1.0	[2019]Multi-Interest Network with Dynamic Routing for Recommendation at Tmall
召回	Word2Vec	✓	✓	✓	x	>=2.1.0	[NIPS 2013]Distributed Representations of Words and Phrases and their Compositionality
召回	DeepWalk	✓	✓	x	x	>=2.1.0	[SIGKDD 2014]DeepWalk: Online Learning of Social Representations
召回	SSR	✓	✓	✓	✓	1.8.5	[SIGIR 2016]Multtti-Rate Deep Learning for Temporal Recommendation
召回	Gru4Rec	✓	✓	✓	✓	1.8.5	[2015]Session-based Recommendations with Recurrent Neural Networks
召回	Youtube_dnn	✓	✓	✓	✓	1.8.5	[RecSys 2016]Deep Neural Networks for YouTube Recommendations
召回	NCF	✓	✓	✓	✓	>=2.1.0	[WWW 2017]Neural Collaborative Filtering
召回	GNN	✓	✓	✓	✓	1.8.5	[AAAI 2019]Session-based Recommendation with Graph Neural Networks
召回	RALM	✓	✓	✓	✓	1.8.5	[KDD 2019]Real-time Attention Based Look-alike Model for Recommender System
排序	Logistic Regression	✓	✓	✓	x	>=2.1.0	/
排序	Dnn	✓	✓	✓	✓	>=2.1.0	/
排序	FM	✓	✓	✓	x	>=2.1.0	[IEEE Data Mining 2010]Factorization machines
排序	FFM	✓	✓	✓	x	>=2.1.0	[RECSYS 2016]Field-aware Factorization Machines for CTR Prediction
排序	FNN	✓	✓	✓	x	1.8.5	[ECIR 2016]Deep Learning over Multi-field Categorical Data
排序	Deep Crossing	✓	✓	✓	x	1.8.5	[ACM 2016]Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features
排序	Pnn	✓	✓	✓	x	1.8.5	[ICDM 2016]Product-based Neural Networks for User Response Prediction
排序	DCN	✓	✓	✓	x	>=2.1.0	[KDD 2017]Deep & Cross Network for Ad Click Predictions
排序	NFM	✓	✓	✓	x	1.8.5	[SIGIR 2017]Neural Factorization Machines for Sparse Predictive Analytics
排序	AFM	✓	✓	✓	x	1.8.5	[IJCAI 2017]Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks
排序	DMR	✓	✓	x	x	>=2.1.0	[AAAI 2020]Deep Match to Rank Model for Personalized Click-Through Rate Prediction
排序	DeepFM	✓	✓	✓	x	>=2.1.0	[IJCAI 2017]DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
排序	xDeepFM	✓	✓	✓	x	>=2.1.0	[KDD 2018]xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
排序	DIN	✓	✓	✓	x	>=2.1.0	[KDD 2018]Deep Interest Network for Click-Through Rate Prediction
排序	DIEN	✓	✓	✓	x	>=2.1.0	[AAAI 2019]Deep Interest Evolution Network for Click-Through Rate Prediction
排序	dlrm	✓	✓	✓	x	>=2.1.0	[CoRR 2019]Deep Learning Recommendation Model for Personalization and Recommendation Systems
排序	DeepFEFM	✓	✓	✓	x	>=2.1.0	[arXiv 2020]Field-Embedded Factorization Machines for Click-through rate prediction
排序	BST	✓	✓	✓	x	1.8.5	[DLP_KDD 2019]Behavior Sequence Transformer for E-commerce Recommendation in Alibaba
排序	AutoInt	✓	✓	✓	x	1.8.5	[CIKM 2019]AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
排序	Wide&Deep	✓	✓	✓	x	>=2.1.0	[DLRS 2016]Wide & Deep Learning for Recommender Systems
排序	FGCNN	✓	✓	✓	✓	1.8.5	[WWW 2019]Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction
排序	Fibinet	✓	✓	✓	✓	1.8.5	[RecSys19]FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction
排序	Flen	✓	✓	✓	✓	1.8.5	[2019]FLEN: Leveraging Field for Scalable CTR Prediction
多任务	PLE	✓	✓	✓	✓	>=2.1.0	[RecSys 2020]Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations
多任务	ESMM	✓	✓	✓	✓	>=2.1.0	[SIGIR 2018]Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate
多任务	MMOE	✓	✓	✓	✓	>=2.1.0	[KDD 2018]Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts
多任务	ShareBottom	✓	✓	✓	✓	>=2.1.0	[1998]Multitask learning
多任务	Maml	✓	✓	x	x	>=2.1.0	[PMLR 2017]Model-agnostic meta-learning for fast adaptation of deep networks
重排序	Listwise	✓	✓	✓	x	1.8.5	[2019]Sequential Evaluation and Generation Framework for Combinatorial Recommender System