# PaddleRec **Repository Path**: smart-platform/PaddleRec ## Basic Information - **Project Name**: PaddleRec - **Description**: 大规模推荐算法库，包含推荐系统经典及最新算法LR、Wide&Deep、DSSM、TDM、MIND、Word2Vec、DeepWalk、SSR、GRU4Rec、Youtube_dnn、NCF、GNN、FM、FFM、DeepFM、DCN、DIN、DIEN、DLRM、MMOE、PLE、ESMM、MAML、xDeepFM、DeepFEFM、NFM、AFM、RALM、Deep Crossing、PNN - **Primary Language**: Python - **License**: Apache-2.0 - **Default Branch**: release/2.2.0 - **Homepage**: https://paddlerec.readthedocs.io/en/latest/ - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 37 - **Created**: 2022-06-26 - **Last Updated**: 2022-06-26 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README ([简体中文](./README.md)|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](https://www.paddlepaddle.org.cn/documentation/docs/en/beginners_guide/index_en.html) - 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 ```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](https://www.paddlepaddle.org.cn/documentation/docs/en/install/index_en.html) ### Download PaddleRec ```bash 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](https://www.kaggle.com/c/criteo-display-ad-challenge/): ```bash 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](doc/rec_background.md) * [Distributed deep learning](doc/ps_background.md) ### Introductory tutorial * [PaddleRec function introduction](doc/introduction.md) * [Dygraph Train](doc/dygraph_mode.md) * [Static Train](doc/static_mode.md) * [Distributed Train](doc/fleet_mode.md) ### Advanced tutorial * [Custom Reader](doc/custom_reader.md) * [Custom Model](doc/model_develop.md) * [Configuration description of yaml](doc/yaml.md) * [Training visualization](doc/visualization.md) * [Serving](doc/serving.md) * [Python inference](doc/inference.md) * [Benchmark](doc/benchmark.md) ### FAQ * [Common Problem FAQ](doc/faq.md)

Support model list

| Type | Algorithm | CPU | GPU | Parameter-Server | Multi-GPU | version | Paper | | :-------------------: | :-----------------------------------------------------------------------: | :---: | :-----: | :--------------: | :-------: | :-------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Content-Understanding | [TextCnn](models/contentunderstanding/textcnn/) | ✓ | ✓ | ✓ | x | >=2.1.0 | [EMNLP 2014][Convolutional neural networks for sentence classication](https://www.aclweb.org/anthology/D14-1181.pdf) | | Content-Understanding | [TagSpace](models/contentunderstanding/tagspace/) | ✓ | ✓ | ✓ | x | >=2.1.0 | [EMNLP 2014][TagSpace: Semantic Embeddings from Hashtags](https://www.aclweb.org/anthology/D14-1194.pdf) | | Match | [DSSM](models/match/dssm/) | ✓ | ✓ | ✓ | x | >=2.1.0 | [CIKM 2013][Learning Deep Structured Semantic Models for Web Search using Clickthrough Data](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/cikm2013_DSSM_fullversion.pdf) | | Match | [MultiView-Simnet](models/match/multiview-simnet/) | ✓ | ✓ | ✓ | x | >=2.1.0 | [WWW 2015][A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/frp1159-songA.pdf) | | Match | [Match-Pyramid](models/match/match-pyramid/) | ✓ | ✓ | ✓ | x | >=2.1.0 | [2016][Text Matching as Image Recognition](https://arxiv.org/pdf/1602.06359.pdf) | | Recall | [TDM](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/treebased/tdm/) | ✓ | >=1.8.0 | ✓ | >=1.8.0 | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [KDD 2018][Learning Tree-based Deep Model for Recommender Systems](https://arxiv.org/pdf/1801.02294.pdf) | | Recall | [fasttext](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/recall/fasttext/) | ✓ | ✓ | x | x |[1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [EACL 2017][Bag of Tricks for Efficient Text Classification](https://www.aclweb.org/anthology/E17-2068.pdf) | | Recall | [MIND](models/recall/mind/) | ✓ | ✓ | x | x | >=2.1.0 | [2019][Multi-Interest Network with Dynamic Routing for Recommendation at Tmall](https://arxiv.org/pdf/1904.08030.pdf) | | Recall | [Word2Vec](models/recall/word2vec/) | ✓ | ✓ | ✓ | x | >=2.1.0 | [NIPS 2013][Distributed Representations of Words and Phrases and their Compositionality](https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf) | | Recall | [DeepWalk](models/recall/deepwalk/) | ✓ | ✓ | x | x | >=2.1.0 | [SIGKDD 2014][DeepWalk: Online Learning of Social Representations](https://arxiv.org/pdf/1403.6652.pdf) | | Recall | [SSR](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/recall/ssr/) | ✓ | ✓ | ✓ | ✓ | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [SIGIR 2016][Multi-Rate Deep Learning for Temporal Recommendation](http://sonyis.me/paperpdf/spr209-song_sigir16.pdf) | | Recall | [Gru4Rec](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/recall/gru4rec/) | ✓ | ✓ | ✓ | ✓ | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [2015][Session-based Recommendations with Recurrent Neural Networks](https://arxiv.org/abs/1511.06939) | | Recall | [Youtube_dnn](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/recall/youtube_dnn/) | ✓ | ✓ | ✓ | ✓ | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [RecSys 2016][Deep Neural Networks for YouTube Recommendations](https://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/45530.pdf) | | Recall | [NCF](models/recall/ncf/) | ✓ | ✓ | ✓ | ✓ | >=2.1.0 | [WWW 2017][Neural Collaborative Filtering](https://arxiv.org/pdf/1708.05031.pdf) | | Recall | [GNN](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/recall/gnn/) | ✓ | ✓ | ✓ | ✓ | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [AAAI 2019][Session-based Recommendation with Graph Neural Networks](https://arxiv.org/abs/1811.00855) | | Recall | [RALM](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/recall/look-alike_recall/) | ✓ | ✓ | ✓ | ✓ | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [KDD 2019][Real-time Attention Based Look-alike Model for Recommender System](https://arxiv.org/pdf/1906.05022.pdf) | | Rank | [Logistic Regression](models/rank/logistic_regression/) | ✓ | ✓ | ✓ | x | >=2.1.0 | / | | Rank | [Dnn](models/rank/dnn/) | ✓ | ✓ | ✓ | ✓ | >=2.1.0 | / | | Rank | [FM](models/rank/fm/) | ✓ | ✓ | ✓ | x | >=2.1.0 | [IEEE Data Mining 2010][Factorization machines](https://analyticsconsultores.com.mx/wp-content/uploads/2019/03/Factorization-Machines-Steffen-Rendle-Osaka-University-2010.pdf) | | Rank | [FFM](models/rank/ffm/) | ✓ | ✓ | ✓ | x | >=2.1.0 | [RECSYS 2016][Field-aware Factorization Machines for CTR Prediction](https://dl.acm.org/doi/pdf/10.1145/2959100.2959134) | | Rank | [FNN](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/fnn/) | ✓ | ✓ | ✓ | x | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [ECIR 2016][Deep Learning over Multi-field Categorical Data](https://arxiv.org/pdf/1601.02376.pdf) | | Rank | [Deep Crossing](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/deep_crossing/) | ✓ | ✓ | ✓ | x | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [ACM 2016][Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features](https://www.kdd.org/kdd2016/papers/files/adf0975-shanA.pdf) | | Rank | [Pnn](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/pnn/) | ✓ | ✓ | ✓ | x | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [ICDM 2016][Product-based Neural Networks for User Response Prediction](https://arxiv.org/pdf/1611.00144.pdf) | | Rank | [DCN](models/rank/dcn/) | ✓ | ✓ | ✓ | x | >=2.1.0 | [KDD 2017][Deep & Cross Network for Ad Click Predictions](https://dl.acm.org/doi/pdf/10.1145/3124749.3124754) | | Rank | [NFM](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/nfm/) | ✓ | ✓ | ✓ | x | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [SIGIR 2017][Neural Factorization Machines for Sparse Predictive Analytics](https://dl.acm.org/doi/pdf/10.1145/3077136.3080777) | | Rank | [AFM](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/afm/) | ✓ | ✓ | ✓ | x | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [IJCAI 2017][Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks](https://arxiv.org/pdf/1708.04617.pdf) | | Rank | [DMR](models/rank/dmr/) | ✓ | ✓ | x | x | >=2.1.0 | [AAAI 2020][Deep Match to Rank Model for Personalized Click-Through Rate Prediction](https://github.com/lvze92/DMR/blob/master/%5BDMR%5D%20Deep%20Match%20to%20Rank%20Model%20for%20Personalized%20Click-Through%20Rate%20Prediction-AAAI20.pdf) | | Rank | [DeepFM](models/rank/deepfm/) | ✓ | ✓ | ✓ | x | >=2.1.0 | [IJCAI 2017][DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://arxiv.org/pdf/1703.04247.pdf) | | Rank | [xDeepFM](models/rank/xdeepfm/) | ✓ | ✓ | ✓ | x | >=2.1.0 | [KDD 2018][xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3219819.3220023) | | Rank | [DIN](models/rank/din/) | ✓ | ✓ | ✓ | x | >=2.1.0 | [KDD 2018][Deep Interest Network for Click-Through Rate Prediction](https://dl.acm.org/doi/pdf/10.1145/3219819.3219823) | | Rank | [DIEN](models/rank/dien/) | ✓ | ✓ | ✓ | x | >=2.1.0 | [AAAI 2019][Deep Interest Evolution Network for Click-Through Rate Prediction](https://www.aaai.org/ojs/index.php/AAAI/article/view/4545/4423) | | Rank | [dlrm](models/rank/dlrm/) | ✓ | ✓ | ✓ | x | >=2.1.0 | [CoRR 2019][Deep Learning Recommendation Model for Personalization and Recommendation Systems](https://arxiv.org/abs/1906.00091) | | Rank | [DeepFEFM](models/rank/deepfefm/) | ✓ | ✓ | ✓ | x | >=2.1.0 | [arXiv 2020][Field-Embedded Factorization Machines for Click-through rate prediction](https://arxiv.org/abs/2009.09931) | | Rank | [BST](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/BST/) | ✓ | ✓ | ✓ | x | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [DLP-KDD 2019][Behavior Sequence Transformer for E-commerce Recommendation in Alibaba](https://arxiv.org/pdf/1905.06874v1.pdf) | | Rank | [AutoInt](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/AutoInt/) | ✓ | ✓ | ✓ | x | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [CIKM 2019][AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://arxiv.org/pdf/1810.11921.pdf) | | Rank | [Wide&Deep](models/rank/wide_deep/) | ✓ | ✓ | ✓ | x | >=2.1.0 | [DLRS 2016][Wide & Deep Learning for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/2988450.2988454) | | Rank | [FGCNN](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/fgcnn/) | ✓ | ✓ | ✓ | ✓ | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [WWW 2019][Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1904.04447.pdf) | | Rank | [Fibinet](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/fibinet/) | ✓ | ✓ | ✓ | ✓ | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [RecSys19][FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction]( https://arxiv.org/pdf/1905.09433.pdf) | | Rank | [Flen](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/flen/) | ✓ | ✓ | ✓ | ✓ | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [2019][FLEN: Leveraging Field for Scalable CTR Prediction]( https://arxiv.org/pdf/1911.04690.pdf) | | Multi-Task | [PLE](models/multitask/ple/) | ✓ | ✓ | ✓ | ✓ | >=2.1.0 | [RecSys 2020][Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations](https://dl.acm.org/doi/abs/10.1145/3383313.3412236) | | Multi-Task | [ESMM](models/multitask/esmm/) | ✓ | ✓ | ✓ | ✓ | >=2.1.0 | [SIGIR 2018][Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://arxiv.org/abs/1804.07931) | | Multi-Task | [MMOE](models/multitask/mmoe/) | ✓ | ✓ | ✓ | ✓ | >=2.1.0 | [KDD 2018][Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts](https://dl.acm.org/doi/abs/10.1145/3219819.3220007) | | Multi-Task | [ShareBottom](models/multitask/share_bottom/) | ✓ | ✓ | ✓ | ✓ | >=2.1.0 | [1998][Multitask learning](http://reports-archive.adm.cs.cmu.edu/anon/1997/CMU-CS-97-203.pdf) | | Multi-Task | [Maml](models/multitask/maml/) | ✓ | ✓ | x | x | >=2.1.0 | [PMLR 2017][Model-agnostic meta-learning for fast adaptation of deep networks](https://arxiv.org/pdf/1703.03400.pdf) | | Re-Rank | [Listwise](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rerank/listwise/) | ✓ | ✓ | ✓ | x | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [2019][Sequential Evaluation and Generation Framework for Combinatorial Recommender System](https://arxiv.org/pdf/1902.00245.pdf) |

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](LICENSE) ### Contact us For any feedback, please propose a [GitHub Issue](https://github.com/PaddlePaddle/PaddleRec/issues) You can also communicate with us in the following ways： - QQ group id：`861717190` - Wechat account：`wxid_0xksppzk5p7f22` - Remarks `REC` add group automatically

     

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