README.md

(简体中文|English)

What is recommendation system ?

• Recommendation system helps users quickly find useful and interesting information from massive data.

• Recommendation system is also a silver bullet to attract users, retain users, increase users' stickness or conversionn.

  Who can better use the recommendation system, who can gain more advantage in the fierce competition.

  At the same time, there are many problems in the process of using the recommendation system, such as: huge data, complex model, inefficient distributed training, and so on.

What is PaddleRec ?

• A quick start tool of search & recommendation algorithm based on PaddlePaddle

• A complete solution of recommendation system for beginners, developers and researchers.

• Recommendation algorithm library including content-understanding, match, recall, rank, multi-task, re-rank etc.

  Type	Algorithm	CPU	GPU	Parameter-Server	Multi-GPU	Paper
  Content-Understanding	Text-Classifcation	✓	✓	✓	x	[EMNLP 2014]Convolutional neural networks for sentence classication
  Content-Understanding	TagSpace	✓	✓	✓	x	[EMNLP 2014]TagSpace: Semantic Embeddings from Hashtags
  Match	DSSM	✓	✓	✓	x	[CIKM 2013]Learning Deep Structured Semantic Models for Web Search using Clickthrough Data
  Match	MultiView-Simnet	✓	✓	✓	x	[WWW 2015]A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems
  Recall	TDM	✓	>=1.8.0	✓	>=1.8.0	[KDD 2018]Learning Tree-based Deep Model for Recommender Systems
  Recall	fasttext	✓	✓	x	x	[EACL 2017]Bag of Tricks for Efficient Text Classification
  Recall	Word2Vec	✓	✓	✓	x	[NIPS 2013]Distributed Representations of Words and Phrases and their Compositionality
  Recall	SSR	✓	✓	✓	✓	[SIGIR 2016]Multi-Rate Deep Learning for Temporal Recommendation
  Recall	Gru4Rec	✓	✓	✓	✓	[2015]Session-based Recommendations with Recurrent Neural Networks
  Recall	Youtube_dnn	✓	✓	✓	✓	[RecSys 2016]Deep Neural Networks for YouTube Recommendations
  Recall	NCF	✓	✓	✓	✓	[WWW 2017]Neural Collaborative Filtering
  Recall	GNN	✓	✓	✓	✓	[AAAI 2019]Session-based Recommendation with Graph Neural Networks
  Rank	Logistic Regression	✓	x	✓	x	/
  Rank	Dnn	✓	✓	✓	✓	/
  Rank	FM	✓	x	✓	x	[IEEE Data Mining 2010]Factorization machines
  Rank	FFM	✓	x	✓	x	[RECSYS 2016]Field-aware Factorization Machines for CTR Prediction
  Rank	FNN	✓	x	✓	x	[ECIR 2016]Deep Learning over Multi-field Categorical Data
  Rank	Deep Crossing	✓	x	✓	x	[ACM 2016]Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features
  Rank	Pnn	✓	x	✓	x	[ICDM 2016]Product-based Neural Networks for User Response Prediction
  Rank	DCN	✓	x	✓	x	[KDD 2017]Deep & Cross Network for Ad Click Predictions
  Rank	NFM	✓	x	✓	x	[SIGIR 2017]Neural Factorization Machines for Sparse Predictive Analytics
  Rank	AFM	✓	x	✓	x	[IJCAI 2017]Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks
  Rank	DeepFM	✓	x	✓	x	[IJCAI 2017]DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
  Rank	xDeepFM	✓	x	✓	x	[KDD 2018]xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
  Rank	DIN	✓	x	✓	x	[KDD 2018]Deep Interest Network for Click-Through Rate Prediction
  Rank	DIEN	✓	x	✓	x	[AAAI 2019]Deep Interest Evolution Network for Click-Through Rate Prediction
  Rank	Wide&Deep	✓	x	✓	x	[DLRS 2016]Wide & Deep Learning for Recommender Systems
  Rank	FGCNN	✓	✓	✓	✓	[WWW 2019]Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction
  Rank	Fibinet	✓	✓	✓	✓	[RecSys19]FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction
  Multi-Task	ESMM	✓	✓	✓	✓	[SIGIR 2018]Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate
  Multi-Task	MMOE	✓	✓	✓	✓	[KDD 2018]Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts
  Multi-Task	ShareBottom	✓	✓	✓	✓	[1998]Multitask learning
  Re-Rank	Listwise	✓	✓	✓	x	[2019]Sequential Evaluation and Generation Framework for Combinatorial Recommender System

Getting Started

Environmental requirements

• Python 2.7/ 3.5 / 3.6 / 3.7

• PaddlePaddle >= 1.7.2

• operating system: Windows/Mac/Linux

  Linux is recommended for distributed training

Installation

1. Install by pip

python -m pip install paddle-rec

This method will download and install paddlepaddle-v1.7.2-cpu. If PaddlePaddle can not be installed automatically，You need to install PaddlePaddle manually，and then install PaddleRec again：

• Download PaddlePaddle and install by pip.
• Install PaddlePaddle by pip，python -m pip install paddlepaddle==1.7.2 -i https://mirror.baidu.com/pypi/simple
• Other installation problems can be raised in Paddle Issue or PaddleRec Issue

2. Install by source code

• Install PaddlePaddle

  python -m pip install paddlepaddle==1.7.2 -i https://mirror.baidu.com/pypi/simple

• Install PaddleRec by source code

  git clone https://github.com/PaddlePaddle/PaddleRec/
  cd PaddleRec
  python setup.py install
  

• Install PaddleRec-GPU

  After installing PaddleRec，please install the appropriate version of paddlepaddle-gpu according to your environment (CUDA / cudnn)，refer to the installation tutorial Installation Manuals

Quick Start

We take the dnn algorithm as an example to get start of PaddleRec, and we take 100 pieces of training data from Criteo Dataset:

# Training with cpu
python -m paddlerec.run -m paddlerec.models.rank.dnn  

Documentation

Background

• Recommendation System
• Distributed deep learning

Introductory Project

• Get start of PaddleRec in ten minutes

Introductory tutorial

• Data
• Model
• Train
• Predict
• Serving

Advanced tutorial

• Custom Reader
• Custom Model
• Custom Training Process
• Configuration description of yaml
• Design document of PaddleRec

Benchmark

• Benchmark

FAQ

• Common Problem FAQ

Community

Version history

• 2020.06.17 - PaddleRec v0.1.0
• 2020.06.03 - PaddleRec v0.0.2
• 2020.05.14 - PaddleRec v0.0.1

License

Apache 2.0 license

Contact us

For any feedback, please propose a GitHub Issue

You can also communicate with us in the following ways：

• QQ group id：861717190
• Wechat account：paddlerec2020

     

PaddleRec QQ Group               PaddleRec Wechat account