github.com/PaddlePaddle/PaddleRec @v2.3.0 sqlite

(中文文档|简体中文|English)

News

• [2022/6/15] Excellent course about multi-task learning application under short video recommendation scenarios，welcome to scan the code and follow：

• [2022/6/15] Add 3 algorithms：ESCM2,MetaHeac,KIM
• [2022/5/18] Add 3 algorithms:：AITM,SIGN,DSIN
• [2022/3/21] Add a new paper directory , show our analysis of the top meeting papers of the recommendation system in 2021 years and the list of recommendation system papers in the industry for your reference.
• [2022/3/10] Add 5 algorithms: DCN_V2, MHCN, FLEN, Dselect_K，AutoFIS。
• [2022/1/12] Add AI Studio Online running function, you can easily and quickly online experience our model on AI studio platform.

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.Support model list

Getting Started

Online running

• AI Studio Online Running

Environmental requirements

• Python 2.7/ 3.5 / 3.6 / 3.7 , Python 3.7 is recommended ,Python in example represents Python 3.7 by default
• PaddlePaddle >=2.0
• operating system: Windows/Mac/Linux

Linux is recommended for distributed training

Installation

• Install by pip in GPU environment bash python -m pip install paddlepaddle-gpu==2.0.0
• Install by pip in CPU environment bash python -m pip install paddlepaddle # gcc8 For download more versions, please refer to the installation tutorial Installation Manuals

Download PaddleRec

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

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:

python -u tools/trainer.py -m models/rank/dnn/config.yaml # Training with dygraph model
python -u tools/static_trainer.py -m models/rank/dnn/config.yaml #  Training with static model

Documentation

Background

• Recommendation System
• Distributed deep Learning

Introductory Tutorial

• PaddleRec Function Introduction
• Dygraph Train
• Static Train
• Distributed Train

Advanced Tutorial

• Submit Specification
• Custom Reader
• Custom Model
• Configuration Description of Yaml
• Training Visualization
• Serving
• Python Inference
• Benchmark
• The latest reserch trends of RS

FAQ

• Common Problem FAQ

Acknowledgements

• Contributions From External Developer

Support_Model_List

Support Model List

Type	Algorithm	Online Environment	Parameter-Server	Multi-GPU	version	Paper
Content-Understanding	TextCnn

(doc) | Python CPU/GPU | ✓ | x | >=2.1.0 | [EMNLP 2014]Convolutional neural networks for sentence classication | | Content-Understanding | TagSpace

(doc) | Python CPU/GPU | ✓ | x | >=2.1.0 | [EMNLP 2014]TagSpace: Semantic Embeddings from Hashtags | | Match | DSSM

(doc) | Python CPU/GPU | ✓ | x | >=2.1.0 | [CIKM 2013]Learning Deep Structured Semantic Models for Web Search using Clickthrough Data | | Match | MultiView-Simnet

(doc) | Python CPU/GPU | ✓ | x | >=2.1.0 | [WWW 2015]A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems | | Match | Match-Pyramid

(doc) | Python CPU/GPU | ✓ | x | >=2.1.0 | [2016]Text Matching as Image Recognition | | Match | KIM(doc) | - | x | x | >=2.1.0 | [WWW 2015]Personalized News Recommendation with Knowledge-aware Interactive Matching | | Recall | TDM | - | ✓ | >=1.8.0 | 1.8.5 | [KDD 2018]Learning Tree-based Deep Model for Recommender Systems | | Recall | FastText | - | x | x |1.8.5 | [EACL 2017]Bag of Tricks for Efficient Text Classification | | Recall | MIND

(doc) | Python CPU/GPU | x | x | >=2.1.0 | [2019]Multi-Interest Network with Dynamic Routing for Recommendation at Tmall | | Recall | Word2Vec

(doc) | Python CPU/GPU | ✓ | x | >=2.1.0 | [NIPS 2013]Distributed Representations of Words and Phrases and their Compositionality | (中文文档|简体中文|English)

News

• [2022/5/18] Add 3 algorithms:：aitm,sign,dsin
• [2022/3/21] Add a new paper directory , show our analysis of the top meeting papers of the recommendation system in 2021 years and the list of recommendation system papers in the industry for your reference.
• [2022/3/10] Add 5 algorithms: DCN_V2, MHCN, FLEN, Dselect_K，AutoFIS。
• [2022/1/12] Add AI Studio Online running function, you can easily and quickly online experience our model on AI studio platform.

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.Support model list

Getting Started

Online running

• AI Studio Online Running

Environmental requirements

• Python 2.7/ 3.5 / 3.6 / 3.7 , Python 3.7 is recommended ,Python in example represents Python 3.7 by default
• PaddlePaddle >=2.0
• operating system: Windows/Mac/Linux

Linux is recommended for distributed training

Installation

• Install by pip in GPU environment bash python -m pip install paddlepaddle-gpu==2.0.0
• Install by pip in CPU environment bash python -m pip install paddlepaddle # gcc8 For download more versions, please refer to the installation tutorial Installation Manuals

Download PaddleRec

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

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:

python -u tools/trainer.py -m models/rank/dnn/config.yaml # Training with dygraph model
python -u tools/static_trainer.py -m models/rank/dnn/config.yaml #  Training with static model

Documentation

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