([简体中文](./README.md)|English)

What is recommendation system ?

- Recommendation system is the key to help users get information of interest efficiently in the era of explosive growth of Internet information - The recommendation system is also a silver bullet to help the product attract users, retain users, increase user stickiness and improve user conversion. - Excellent recommendation system can help the product establish a good reputation, and help the product gain market share > It can be said that who can master and make good use of the recommendation system, who can get the first chance in the fierce competition of information distribution. > > At the same time, there are many problems that perplex the developers of the recommendation system, such as: huge data volume, complex model structure, inefficient distributed training environment, demanding online deployment requirements, all of which are too numerous to enumerate.

What is PaddleRec ?

- A quick start tool of search & recommendation model based on [PaddlePaddle](https://www.paddlepaddle.org.cn/documentation/docs/en/beginners_guide/index_en.html) - The whole process solution of recommendation system for beginners, developers and researchers - Complete recommendation algorithm library including content understanding, matching, recall, ranking, multi-task, re-rank etc. | Type | Algorithm | CPU | GPU | Parameter-Server | Multi-GPU | Paper | | :-------------------: | :-----------------------------------------------------------------------: | :---: | :-----: | :--------------: | :-------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Content-Understanding | [Text-Classifcation](models/contentunderstanding/classification/model.py) | ✓ | ✓ | ✓ | x | [EMNLP 2014][Convolutional neural networks for sentence classication](https://www.aclweb.org/anthology/D14-1181.pdf) | | Content-Understanding | [TagSpace](models/contentunderstanding/tagspace/model.py) | ✓ | ✓ | ✓ | x | [EMNLP 2014][TagSpace: Semantic Embeddings from Hashtags](https://www.aclweb.org/anthology/D14-1194.pdf) | | Matching | [DSSM](models/match/dssm/model.py) | ✓ | ✓ | ✓ | x | [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) | | Matching | [MultiView-Simnet](models/match/multiview-simnet/model.py) | ✓ | ✓ | ✓ | x | [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) | | Recall | [TDM](models/treebased/tdm/model.py) | ✓ | >=1.8.0 | ✓ | >=1.8.0 | [KDD 2018][Learning Tree-based Deep Model for Recommender Systems](https://arxiv.org/pdf/1801.02294.pdf) | | Recall | [fasttext](models/recall/fasttext/model.py) | ✓ | ✓ | x | x | [EACL 2017][Bag of Tricks for Efficient Text Classification](https://www.aclweb.org/anthology/E17-2068.pdf) | | Recall | [Word2Vec](models/recall/word2vec/model.py) | ✓ | ✓ | ✓ | x | [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 | [SSR](models/recall/ssr/model.py) | ✓ | ✓ | ✓ | ✓ | [SIGIR 2016][Multi-Rate Deep Learning for Temporal Recommendation](http://sonyis.me/paperpdf/spr209-song_sigir16.pdf) | | Recall | [Gru4Rec](models/recall/gru4rec/model.py) | ✓ | ✓ | ✓ | ✓ | [2015][Session-based Recommendations with Recurrent Neural Networks](https://arxiv.org/abs/1511.06939) | | Recall | [Youtube_dnn](models/recall/youtube_dnn/model.py) | ✓ | ✓ | ✓ | ✓ | [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/model.py) | ✓ | ✓ | ✓ | ✓ | [WWW 2017][Neural Collaborative Filtering](https://arxiv.org/pdf/1708.05031.pdf) | | Recall | [GNN](models/recall/gnn/model.py) | ✓ | ✓ | ✓ | ✓ | [AAAI 2019][Session-based Recommendation with Graph Neural Networks](https://arxiv.org/abs/1811.00855) | | Ranking | [Logistic Regression](models/rank/logistic_regression/model.py) | ✓ | x | ✓ | x | / | | Ranking | [Dnn](models/rank/dnn/model.py) | ✓ | ✓ | ✓ | ✓ | / | | Ranking | [FM](models/rank/fm/model.py) | ✓ | x | ✓ | x | [IEEE Data Mining 2010][Factorization machines](https://analyticsconsultores.com.mx/wp-content/uploads/2019/03/Factorization-Machines-Steffen-Rendle-Osaka-University-2010.pdf) | | Ranking | [FFM](models/rank/ffm/model.py) | ✓ | x | ✓ | x | [RECSYS 2016][Field-aware Factorization Machines for CTR Prediction](https://dl.acm.org/doi/pdf/10.1145/2959100.2959134) | | Ranking | [FNN](models/rank/fnn/model.py) | ✓ | x | ✓ | x | [ECIR 2016][Deep Learning over Multi-field Categorical Data](https://arxiv.org/pdf/1601.02376.pdf) | | Ranking | [Deep Crossing](models/rank/deep_crossing/model.py) | ✓ | x | ✓ | x | [ACM 2016][Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features](https://www.kdd.org/kdd2016/papers/files/adf0975-shanA.pdf) | | Ranking | [Pnn](models/rank/pnn/model.py) | ✓ | x | ✓ | x | [ICDM 2016][Product-based Neural Networks for User Response Prediction](https://arxiv.org/pdf/1611.00144.pdf) | | Ranking | [DCN](models/rank/dcn/model.py) | ✓ | x | ✓ | x | [KDD 2017][Deep & Cross Network for Ad Click Predictions](https://dl.acm.org/doi/pdf/10.1145/3124749.3124754) | | Ranking | [NFM](models/rank/nfm/model.py) | ✓ | x | ✓ | x | [SIGIR 2017][Neural Factorization Machines for Sparse Predictive Analytics](https://dl.acm.org/doi/pdf/10.1145/3077136.3080777) | | Ranking | [AFM](models/rank/afm/model.py) | ✓ | x | ✓ | x | [IJCAI 2017][Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks](https://arxiv.org/pdf/1708.04617.pdf) | | Ranking | [DeepFM](models/rank/deepfm/model.py) | ✓ | x | ✓ | x | [IJCAI 2017][DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://arxiv.org/pdf/1703.04247.pdf) | | Ranking | [xDeepFM](models/rank/xdeepfm/model.py) | ✓ | x | ✓ | x | [KDD 2018][xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3219819.3220023) | | Ranking | [DIN](models/rank/din/model.py) | ✓ | x | ✓ | x | [KDD 2018][Deep Interest Network for Click-Through Rate Prediction](https://dl.acm.org/doi/pdf/10.1145/3219819.3219823) | | Ranking | [Wide&Deep](models/rank/wide_deep/model.py) | ✓ | x | ✓ | x | [DLRS 2016][Wide & Deep Learning for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/2988450.2988454) | | Ranking | [FGCNN](models/rank/fgcnn/model.py) | ✓ | ✓ | ✓ | ✓ | [WWW 2019][Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1904.04447.pdf) | | Ranking | [Fibinet](models/rank/fibinet/model.py) | ✓ | ✓ | ✓ | ✓ | [RecSys19][FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction]( https://arxiv.org/pdf/1905.09433.pdf) | | Multi-Task | [ESMM](models/multitask/esmm/model.py) | ✓ | ✓ | ✓ | ✓ | [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/model.py) | ✓ | ✓ | ✓ | ✓ | [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/model.py) | ✓ | ✓ | ✓ | ✓ | [1998][Multitask learning](http://reports-archive.adm.cs.cmu.edu/anon/1997/CMU-CS-97-203.pdf) | | Re-Rank | [Listwise](models/rerank/listwise/model.py) | ✓ | ✓ | ✓ | x | [2019][Sequential Evaluation and Generation Framework for Combinatorial Recommender System](https://arxiv.org/pdf/1902.00245.pdf) |

Getting Started

### Environmental requirements * Python 2.7/ 3.5 / 3.6 / 3.7 * PaddlePaddle >= 1.7.2 * operating system: Windows/Mac/Linux > Linux is recommended for distributed training ### Installation 1. **Install by pip** ```bash python -m pip install paddle-rec ``` > This method will download and install`paddlepaddle-v1.7.2-cpu`，if you are prompted that `PaddlePaddle` can not be installed automatically，You need to install `PaddlePaddle` manually，and then install `Paddlerec` again： > - Download PaddlePaddle whl from [address](https://pypi.org/project/paddlepaddle/1.7.2/#files) and install by pip. > - Directly install `PaddlePaddle` by pip，`python -m pip install paddlepaddle==1.7.2 -i https://mirror.baidu.com/pypi/simple` > - Other installation problems can be raised in [Paddle Issue](https://github.com/PaddlePaddle/Paddle/issues) or [PaddleRec Issue](https://github.com/PaddlePaddle/PaddleRec/issues) 2. **Install by source code** - Install PaddlePaddle ```shell python -m pip install paddlepaddle==1.7.2 -i https://mirror.baidu.com/pypi/simple ``` - Install PaddleRec by source code ``` git clone https://github.com/PaddlePaddle/PaddleRec/ cd PaddleRec python setup.py install ``` - Install PaddleRec-GPU After installing `PaddleRec`，You need to manually install `paddlepaddle-gpu`，select the appropriate version according to your environment (CUDA / cudnn)，please refer to the installation tutorial[Installation Manuals](https://www.paddlepaddle.org.cn/documentation/docs/en/install/index_en.html)

Quick Start

We take the `dnn` algorithm as an example to introduce the quick start of `PaddleRec`, and we took 100 pieces of training data from [Criteo Dataset](https://www.kaggle.com/c/criteo-display-ad-challenge/): ```bash # Training with cpu python -m paddlerec.run -m paddlerec.models.rank.dnn ```

Documentation

### Background * [Recommendation System](doc/rec_background.md) * [Distributed deep learning](doc/ps_background.md) ### Introductory Project * [Ten minutes to learn PaddleRec](https://aistudio.baidu.com/aistudio/projectdetail/559336) ### Introductory tutorial * [Prepare Data](doc/slot_reader.md) * [HyperParameter of model](doc/model.md) * [Start Training](doc/train.md) * [Start Predicting](doc/predict.md) * [Serving](doc/serving.md) ### Advanced tutorial * [Custom Reader](doc/custom_reader.md) * [Custom Model](doc/model_develop.md) * [Custom Training Process](doc/trainer_develop.md) * [Configuration description of yaml](doc/yaml.md) * [Design document of PaddleRec](doc/design.md) ### Benchmark * [Benchmark](doc/benchmark.md) ### FAQ * [Common Problem FAQ](doc/faq.md)

Community


Release License Slack

### Version history - 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) ### Contack us For any feedback or to report a bug, 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：`paddlerec2020`

     

PaddleRec QQ Group               PaddleRec Wechat account