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(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,IPRec
  • [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

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 
    python -m pip install paddlepaddle-gpu==2.0.0
  • Install by pip in CPU environment 
    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

Introductory Tutorial

Advanced Tutorial

FAQ

Acknowledgements

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
Recall DeepWalk
(doc)		 Python CPU/GPU x x >=2.1.0 [SIGKDD 2014]DeepWalk: Online Learning of Social Representations
Recall SSR - ✓ ✓ 1.8.5 [SIGIR 2016]Multi-Rate Deep Learning for Temporal Recommendation
Recall Gru4Rec
(doc)		 - ✓ ✓ 1.8.5 [2015]Session-based Recommendations with Recurrent Neural Networks
Recall Youtube_dnn - ✓ ✓ 1.8.5 [RecSys 2016]Deep Neural Networks for YouTube Recommendations
Recall NCF
(doc)		 Python CPU/GPU ✓ ✓ >=2.1.0 [WWW 2017]Neural Collaborative Filtering
Recall TiSAS - ✓ ✓ >=2.1.0 [WSDM 2020]Time Interval Aware Self-Attention for Sequential Recommendation
Recall ENSFM - ✓ ✓ >=2.1.0 [IW3C2 2020]Eicient Non-Sampling Factorization Machines for Optimal Context-Aware Recommendation
Recall MHCN - ✓ ✓ >=2.1.0 [WWW 2021]Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation
Recall GNN - ✓ ✓ 1.8.5 [AAAI 2019]Session-based Recommendation with Graph Neural Networks
Recall RALM - ✓ ✓ 1.8.5 [KDD 2019]Real-time Attention Based Look-alike Model for Recommender System
Rank Logistic Regression
(doc)		 Python CPU/GPU ✓ x >=2.1.0 /
Rank Dnn
(doc)		 Python CPU/GPU ✓ ✓ >=2.1.0 /
Rank FM
(doc)		 Python CPU/GPU ✓ x >=2.1.0 [IEEE Data Mining 2010]Factorization machines
Rank BERT4REC - ✓ x >=2.1.0 [CIKM 2019]BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
Rank FAT_DeepFFM - ✓ x >=2.1.0 [2019]FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine
Rank FFM
(doc)		 Python CPU/GPU ✓ x >=2.1.0 [RECSYS 2016]Field-aware Factorization Machines for CTR Prediction
Rank FNN - ✓ x 1.8.5 [ECIR 2016]Deep Learning over Multi-field Categorical Data
Rank Deep Crossing - ✓ x 1.8.5 [ACM 2016]Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features
Rank Pnn - ✓ x 1.8.5 [ICDM 2016]Product-based Neural Networks for User Response Prediction
Rank DCN
(doc)		 Python CPU/GPU ✓ x >=2.1.0 [KDD 2017]Deep & Cross Network for Ad Click Predictions
Rank NFM - ✓ x 1.8.5 [SIGIR 2017]Neural Factorization Machines for Sparse Predictive Analytics
Rank AFM - ✓ x 1.8.5 [IJCAI 2017]Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks
Rank DMR
(doc)		 Python CPU/GPU x x >=2.1.0 [AAAI 2020]Deep Match to Rank Model for Personalized Click-Through Rate Prediction
Rank DeepFM
(doc)		 Python CPU/GPU ✓ x >=2.1.0 [IJCAI 2017]DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
Rank xDeepFM
(doc)		 Python CPU/GPU ✓ x >=2.1.0 [KDD 2018]xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
Rank DIN
(doc)		 Python CPU/GPU ✓ x >=2.1.0 [KDD 2018]Deep Interest Network for Click-Through Rate Prediction
Rank DIEN
(doc)		 Python CPU/GPU ✓ x >=2.1.0 [AAAI 2019]Deep Interest Evolution Network for Click-Through Rate Prediction
Rank GateNet
(doc)		 Python CPU/GPU ✓ x >=2.1.0 [SIGIR 2020]GateNet: Gating-Enhanced Deep Network for Click-Through Rate Prediction
Rank DLRM
(doc)		 Python CPU/GPU ✓ x >=2.1.0 [CoRR 2019]Deep Learning Recommendation Model for Personalization and Recommendation Systems
Rank NAML
(doc)		 Python CPU/GPU ✓ x >=2.1.0 [IJCAI 2019]Neural News Recommendation with Attentive Multi-View Learning
Rank DIFM
(doc)		 Python CPU/GPU ✓ x >=2.1.0 [IJCAI 2020]A Dual Input-aware Factorization Machine for CTR Prediction
Rank DeepFEFM
(doc)		 Python CPU/GPU ✓ x >=2.1.0 [arXiv 2020]Field-Embedded Factorization Machines for Click-through rate prediction
Rank BST
(doc)		 Python CPU/GPU ✓ x >=2.1.0 [DLP-KDD 2019]Behavior Sequence Transformer for E-commerce Recommendation in Alibaba
Rank AutoInt - ✓ x >=2.1.0 [CIKM 2019]AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
Rank Wide&Deep
(doc)		 Python CPU/GPU ✓ x >=2.1.0 [DLRS 2016]Wide & Deep Learning for Recommender Systems
Rank Fibinet - ✓ ✓ 1.8.5 [RecSys19]FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction
Rank FLEN - ✓ ✓ >=2.1.0 [2019]FLEN: Leveraging Field for Scalable CTR Prediction
Rank DeepRec - ✓ ✓ >=2.1.0 [2017]Training Deep AutoEncoders for Collaborative Filtering
Rank AutoFIS - ✓ ✓ >=2.1.0 [KDD 2020]AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction
Rank DCN_V2 - ✓ ✓ >=2.1.0 [WWW 2021]DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems
Rank DSIN - ✓ ✓ >=2.1.0 [IJCAI 2019]Deep Session Interest Network for Click-Through Rate Prediction
Rank SIGN(doc) Python CPU/GPU ✓ ✓ >=2.1.0 [AAAI 2021]Detecting Beneficial Feature Interactions for Recommender Systems
Rank FGCNN - ✓ ✓ >=2.1.0 [WWW 2019]Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction
Rank IPRec(doc) - ✓ ✓ >=2.1.0 [SIGIR 2021]Package Recommendation with Intra- and Inter-Package Attention Networks
Rank DPIN(doc) Python CPU/GPU ✓ ✓ >=2.1.0 [SIGIR 2021]Deep Position-wise Interaction Network for CTR Prediction
Multi-Task AITM - ✓ ✓ >=2.1.0 [KDD 2021]Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising
Multi-Task PLE
(doc)		 Python CPU/GPU ✓ ✓ >=2.1.0 [RecSys 2020]Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations
Multi-Task ESMM
(doc)		 Python CPU/GPU ✓ ✓ >=2.1.0 [SIGIR 2018]Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate
Multi-Task MMOE
(doc)		 Python CPU/GPU ✓ ✓ >=2.1.0 [KDD 2018]Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts
Multi-Task ShareBottom
(doc)		 Python CPU/GPU ✓ ✓ >=2.1.0 [1998]Multitask learning
Multi-Task Maml
(doc)		 Python CPU/GPU x x >=2.1.0 [PMLR 2017]Model-agnostic meta-learning for fast adaptation of deep networks
Multi-Task DSelect_K
(doc)		 - x x >=2.1.0 [NeurIPS 2021]DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning
Multi-Task ESCM2 - x x >=2.1.0 [SIGIR 2022]ESCM2: Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation
Multi-Task MetaHeac - x x >=2.1.0 [KDD 2021]Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising
Re-Rank Listwise - ✓ x 1.8.5 [2019]Sequential Evaluation and Generation Framework for Combinatorial Recommender System

Community


Release License Slack

Version history

  • 2022.06.20 - PaddleRec v2.3.0
  • 2021.11.19 - PaddleRec v2.2.0
  • 2021.05.19 - PaddleRec v2.1.0
  • 2021.01.29 - PaddleRec v2.0.0
  • 2020.10.12 - PaddleRec v1.8.5
  • 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：wxid_0xksppzk5p7f22
  • Remarks REC add group automatically

     

PaddleRec QQ Group               PaddleRec Wechat account