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(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.

Getting Started

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

Support model list

Type Algorithm CPU GPU Parameter-Server Multi-GPU version Paper
Content-Understanding TextCnn ✓ ✓ ✓ x >=2.1.0 [EMNLP 2014]Convolutional neural networks for sentence classication
Content-Understanding TagSpace ✓ ✓ ✓ x >=2.1.0 [EMNLP 2014]TagSpace: Semantic Embeddings from Hashtags
Match DSSM ✓ ✓ ✓ x >=2.1.0 [CIKM 2013]Learning Deep Structured Semantic Models for Web Search using Clickthrough Data
Match MultiView-Simnet ✓ ✓ ✓ x >=2.1.0 [WWW 2015]A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems
Match Match-Pyramid ✓ ✓ ✓ x >=2.1.0 [2016]Text Matching as Image Recognition
Recall TDM ✓ >=1.8.0 ✓ >=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 ✓ ✓ x x >=2.1.0 [2019]Multi-Interest Network with Dynamic Routing for Recommendation at Tmall
Recall Word2Vec ✓ ✓ ✓ x >=2.1.0 [NIPS 2013]Distributed Representations of Words and Phrases and their Compositionality
Recall DeepWalk ✓ ✓ 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 ✓ ✓ ✓ ✓ 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 ✓ ✓ ✓ ✓ >=2.1.0 [WWW 2017]Neural Collaborative Filtering
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 ✓ ✓ ✓ x >=2.1.0 /
Rank Dnn ✓ ✓ ✓ ✓ >=2.1.0 /
Rank FM ✓ ✓ ✓ x >=2.1.0 [IEEE Data Mining 2010]Factorization machines
Rank FFM ✓ ✓ ✓ 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 ✓ ✓ ✓ 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 ✓ ✓ x x >=2.1.0 [AAAI 2020]Deep Match to Rank Model for Personalized Click-Through Rate Prediction
Rank DeepFM ✓ ✓ ✓ x >=2.1.0 [IJCAI 2017]DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
Rank xDeepFM ✓ ✓ ✓ x >=2.1.0 [KDD 2018]xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
Rank DIN ✓ ✓ ✓ x >=2.1.0 [KDD 2018]Deep Interest Network for Click-Through Rate Prediction
Rank DIEN ✓ ✓ ✓ x >=2.1.0 [AAAI 2019]Deep Interest Evolution Network for Click-Through Rate Prediction
Rank dlrm ✓ ✓ ✓ x >=2.1.0 [CoRR 2019]Deep Learning Recommendation Model for Personalization and Recommendation Systems
Rank DeepFEFM ✓ ✓ ✓ x >=2.1.0 [arXiv 2020]Field-Embedded Factorization Machines for Click-through rate prediction
Rank BST ✓ ✓ ✓ x 1.8.5 [DLP-KDD 2019]Behavior Sequence Transformer for E-commerce Recommendation in Alibaba
Rank AutoInt ✓ ✓ ✓ x 1.8.5 [CIKM 2019]AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
Rank Wide&Deep ✓ ✓ ✓ x >=2.1.0 [DLRS 2016]Wide & Deep Learning for Recommender Systems
Rank FGCNN ✓ ✓ ✓ ✓ 1.8.5 [WWW 2019]Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction
Rank Fibinet ✓ ✓ ✓ ✓ 1.8.5 [RecSys19]FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction
Rank Flen ✓ ✓ ✓ ✓ 1.8.5 [2019]FLEN: Leveraging Field for Scalable CTR Prediction
Multi-Task PLE ✓ ✓ ✓ ✓ >=2.1.0 [RecSys 2020]Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations
Multi-Task ESMM ✓ ✓ ✓ ✓ >=2.1.0 [SIGIR 2018]Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate
Multi-Task MMOE ✓ ✓ ✓ ✓ >=2.1.0 [KDD 2018]Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts
Multi-Task ShareBottom ✓ ✓ ✓ ✓ >=2.1.0 [1998]Multitask learning
Multi-Task Maml ✓ ✓ x x >=2.1.0 [PMLR 2017]Model-agnostic meta-learning for fast adaptation of deep networks
Re-Rank Listwise ✓ ✓ ✓ x 1.8.5 [2019]Sequential Evaluation and Generation Framework for Combinatorial Recommender System

Community


Release License Slack

Version history

  • 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