Awesome Open Source
Awesome Open Source




  • Python 2.7.15 / 3.5 / 3.6 / 3.7, python3.7pythonpython3.7

  • PaddlePaddle >=2.0

  • : Windows/Mac/Linux



  • gpupip
    python -m pip install paddlepaddle-gpu==2.0.0 
  • cpupip
    python -m pip install paddlepaddle # gcc8 




git clone
cd PaddleRec


python -u tools/ -m models/rank/dnn/config.yaml #  
python -u tools/ -m models/rank/dnn/config.yaml #  


TextCnn() Python CPU/GPU x >=2.1.0 [EMNLP 2014]Convolutional neural networks for sentence classication
TagSpace() Python CPU/GPU x >=2.1.0 [EMNLP 2014]TagSpace: Semantic Embeddings from Hashtags
DSSM() Python CPU/GPU x >=2.1.0 [CIKM 2013]Learning Deep Structured Semantic Models for Web Search using Clickthrough Data
Match-Pyramid() Python CPU/GPU x >=2.1.0 [AAAI 2016]Text Matching as Image Recognition
MultiView-Simnet() Python CPU/GPU x >=2.1.0 [WWW 2015]A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems
KIM() - x x >=2.1.0 [SIGIR 2021]Personalized News Recommendation with Knowledge-aware Interactive Matching
TDM - >=1.8.0 1.8.5 [KDD 2018]Learning Tree-based Deep Model for Recommender Systems
FastText - x x 1.8.5 [EACL 2017]Bag of Tricks for Efficient Text Classification
MIND() Python CPU/GPU x x >=2.1.0 [2019]Multi-Interest Network with Dynamic Routing for Recommendation at Tmall
Word2Vec() Python CPU/GPU x >=2.1.0 [NIPS 2013]Distributed Representations of Words and Phrases and their Compositionality
DeepWalk() Python CPU/GPU x x >=2.1.0 [SIGKDD 2014]DeepWalk: Online Learning of Social Representations
SSR - 1.8.5 [SIGIR 2016]Multtti-Rate Deep Learning for Temporal Recommendation
Gru4Rec() - 1.8.5 [2015]Session-based Recommendations with Recurrent Neural Networks
Youtube_dnn - 1.8.5 [RecSys 2016]Deep Neural Networks for YouTube Recommendations
NCF() Python CPU/GPU >=2.1.0 [WWW 2017]Neural Collaborative Filtering
TiSAS - >=2.1.0 [WSDM 2020]Time Interval Aware Self-Attention for Sequential Recommendation
ENSFM - >=2.1.0 [IW3C2 2020]Eicient Non-Sampling Factorization Machines for Optimal Context-Aware Recommendation
MHCN - >=2.1.0 [WWW 2021]Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation
GNN - 1.8.5 [AAAI 2019]Session-based Recommendation with Graph Neural Networks
RALM - 1.8.5 [KDD 2019]Real-time Attention Based Look-alike Model for Recommender System
Logistic Regression() Python CPU/GPU x >=2.1.0 /
Dnn() Python CPU/GPU >=2.1.0 /
FM() Python CPU/GPU x >=2.1.0 [IEEE Data Mining 2010]Factorization machines
BERT4REC - x >=2.1.0 [CIKM 2019]BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
FAT_DeepFFM - x >=2.1.0 [2019]FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine
FFM() Python CPU/GPU x >=2.1.0 [RECSYS 2016]Field-aware Factorization Machines for CTR Prediction
FNN - x 1.8.5 [ECIR 2016]Deep Learning over Multi-field Categorical Data
Deep Crossing - x 1.8.5 [ACM 2016]Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features
Pnn - x 1.8.5 [ICDM 2016]Product-based Neural Networks for User Response Prediction
DCN() Python CPU/GPU x >=2.1.0 [KDD 2017]Deep & Cross Network for Ad Click Predictions
NFM - x 1.8.5 [SIGIR 2017]Neural Factorization Machines for Sparse Predictive Analytics
AFM - x 1.8.5 [IJCAI 2017]Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks
DMR() Python CPU/GPU x x >=2.1.0 [AAAI 2020]Deep Match to Rank Model for Personalized Click-Through Rate Prediction
DeepFM() Python CPU/GPU x >=2.1.0 [IJCAI 2017]DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
xDeepFM() Python CPU/GPU x >=2.1.0 [KDD 2018]xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
DIN() Python CPU/GPU x >=2.1.0 [KDD 2018]Deep Interest Network for Click-Through Rate Prediction
DIEN() Python CPU/GPU x >=2.1.0 [AAAI 2019]Deep Interest Evolution Network for Click-Through Rate Prediction
GateNet() Python CPU/GPU x >=2.1.0 [SIGIR 2020]GateNet: Gating-Enhanced Deep Network for Click-Through Rate Prediction
DLRM() Python CPU/GPU x >=2.1.0 [CoRR 2019]Deep Learning Recommendation Model for Personalization and Recommendation Systems
NAML() Python CPU/GPU x >=2.1.0 [IJCAI 2019]Neural News Recommendation with Attentive Multi-View Learning
DIFM() Python CPU/GPU x >=2.1.0 [IJCAI 2020]A Dual Input-aware Factorization Machine for CTR Prediction
DeepFEFM() Python CPU/GPU x >=2.1.0 [arXiv 2020]Field-Embedded Factorization Machines for Click-through rate prediction
BST() Python CPU/GPU x >=2.1.0 [DLP_KDD 2019]Behavior Sequence Transformer for E-commerce Recommendation in Alibaba
AutoInt - x >=2.1.0 [CIKM 2019]AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
Wide&Deep() Python CPU/GPU x >=2.1.0 [DLRS 2016]Wide & Deep Learning for Recommender Systems
Fibinet - 1.8.5 [RecSys19]FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction
FLEN - >=2.1.0 [2019]FLEN: Leveraging Field for Scalable CTR Prediction
DeepRec - >=2.1.0 [2017]Training Deep AutoEncoders for Collaborative Filtering
AutoFIS - >=2.1.0 [KDD 2020]AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction
DCN_V2 - >=2.1.0 [WWW 2021]DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems
DSIN - >=2.1.0 [IJCAI 2019]Deep Session Interest Network for Click-Through Rate Prediction
SIGN() Python CPU/GPU >=2.1.0 [AAAI 2021]Detecting Beneficial Feature Interactions for Recommender Systems
IPRec() - >=2.1.0 [SIGIR 2021]Package Recommendation with Intra- and Inter-Package Attention Networks
FGCNN - >=2.1.0 [WWW 2019]Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction
DPIN() Python CPU/GPU >=2.1.0 [SIGIR 2021]Deep Position-wise Interaction Network for CTR Prediction
AITM - >=2.1.0 [KDD 2021]Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising
PLE() Python CPU/GPU >=2.1.0 [RecSys 2020]Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations
ESMM() Python CPU/GPU >=2.1.0 [SIGIR 2018]Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate
MMOE() Python CPU/GPU >=2.1.0 [KDD 2018]Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts
ShareBottom() Python CPU/GPU >=2.1.0 [1998]Multitask learning
Maml() Python CPU/GPU x x >=2.1.0 [PMLR 2017]Model-agnostic meta-learning for fast adaptation of deep networks
DSelect_K() - x x >=2.1.0 [NeurIPS 2021]DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning
ESCM2 - x x >=2.1.0 [SIGIR 2022]ESCM2: Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation
MetaHeac - x x >=2.1.0 [KDD 2021]Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising
Listwise - x 1.8.5 [2019]Sequential Evaluation and Generation Framework for Combinatorial Recommender System

Release License Slack

  • 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

Apache 2.0 license

BUGGitHub Issue

  • QQ861717190
  • wxid_0xksppzk5p7f22
  • REC


PaddleRecQQ               PaddleRec

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