# PaddleRec
**Repository Path**: pxw00611/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**: master
- **Homepage**: https://paddlerec.readthedocs.io/en/latest/
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 38
- **Created**: 2024-04-30
- **Last Updated**: 2024-04-30
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
([中文文档](https://paddlerec.readthedocs.io/en/latest/)|[简体中文](./README_CN.md)|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](models/multitask/escm2),[MetaHeac](models/multitask/metaheac),[KIM](models/match/kim)
* [2022/5/18] Add 3 algorithms:：[AITM](models/multitask/aitm),[SIGN](models/rank/sign),[DSIN](models/rank/dsin),[IPRec](models/rank/iprec)
* [2022/3/21] Add a new [paper](./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](models/rank/dcn_v2), [MHCN](models/recall/mhcn), [FLEN](models/rank/flen), [Dselect_K](models/multitask/dselect_k)，[AutoFIS](models/rank/autofis)。
* [2022/1/12] Add AI Studio [Online running](https://aistudio.baidu.com/aistudio/projectdetail/3240640) 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](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.[Support model list](#Support_Model_List)
Getting Started
### Online running
- **[AI Studio Online Running](https://aistudio.baidu.com/aistudio/projectdetail/3240640)**
### 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
* [Submit Specification](doc/contribute.md)
* [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)
* [The latest reserch trends of RS](paper/readme.md)
### FAQ
* [Common Problem FAQ](doc/faq.md)
### Acknowledgements
* [Contributions From External Developer](contributor.md)
### Support_Model_List
Support Model List
| Type | Algorithm | Online Environment | Parameter-Server | Multi-GPU | version | Paper |
| :-------------------: | :-----------------------------------------------------------------------: | :---: | :--------------: | :-------: | :-------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Content-Understanding | [TextCnn](models/contentunderstanding/textcnn/)
([doc](https://paddlerec.readthedocs.io/en/latest/models/contentunderstanding/textcnn.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238415) | ✓ | 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/)
([doc](https://paddlerec.readthedocs.io/en/latest/models/contentunderstanding/tagspace.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238891) | ✓ | x | >=2.1.0 | [EMNLP 2014][TagSpace: Semantic Embeddings from Hashtags](https://www.aclweb.org/anthology/D14-1194.pdf) |
| Match | [DSSM](models/match/dssm/)
([doc](https://paddlerec.readthedocs.io/en/latest/models/match/dssm.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3217658?contributionType=1) | ✓ | 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/)
([doc](https://paddlerec.readthedocs.io/en/latest/models/match/multiview-simnet.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238206) | ✓ | 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/)
([doc](https://paddlerec.readthedocs.io/en/latest/models/match/match-pyramid.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238192) | ✓ | x | >=2.1.0 | [2016][Text Matching as Image Recognition](https://arxiv.org/pdf/1602.06359.pdf) |
| Match | [KIM](models/match/kim/)([doc](https://paddlerec.readthedocs.io/en/latest/models/match/kim.html)) | - | x | x | >=2.1.0 | [WWW 2015][Personalized News Recommendation with Knowledge-aware Interactive Matching](https://arxiv.org/pdf/2104.10083.pdf) |
| Recall | [TDM](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/treebased/tdm/) | - | ✓ | >=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/)
([doc](https://paddlerec.readthedocs.io/en/latest/models/recall/mind.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3239088) | 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/)
([doc](https://paddlerec.readthedocs.io/en/latest/models/recall/word2vec.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3240153) | ✓ | 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/)
([doc](https://paddlerec.readthedocs.io/en/latest/models/recall/deepwalk.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3239086) | 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/)
([doc](https://paddlerec.readthedocs.io/en/latest/models/recall/gru4rec.html)) | - | ✓ | ✓ | [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/)
([doc](https://paddlerec.readthedocs.io/en/latest/models/recall/ncf.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3240152) | ✓ | ✓ | >=2.1.0 | [WWW 2017][Neural Collaborative Filtering](https://arxiv.org/pdf/1708.05031.pdf) |
| Recall | [TiSAS](models/recall/tisas/) | - | ✓ | ✓ | >=2.1.0 | [WSDM 2020][Time Interval Aware Self-Attention for Sequential Recommendation](https://cseweb.ucsd.edu/~jmcauley/pdfs/wsdm20b.pdf) |
| Recall | [ENSFM](models/recall/ensfm/) | - | ✓ | ✓ | >=2.1.0 | [IW3C2 2020][Eicient Non-Sampling Factorization Machines for Optimal Context-Aware Recommendation](http://www.thuir.cn/group/~mzhang/publications/TheWebConf2020-Chenchong.pdf) |
| Recall | [MHCN](models/recall/mhcn/) | - | ✓ | ✓ | >=2.1.0 | [WWW 2021][Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation](https://arxiv.org/pdf/2101.06448v3.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/)
([doc](https://paddlerec.readthedocs.io/en/latest/models/rank/logistic_regression.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3240481) | ✓ | x | >=2.1.0 | / |
| Rank | [Dnn](models/rank/dnn/)
([doc](https://paddlerec.readthedocs.io/en/latest/models/rank/dnn.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3240347) | ✓ | ✓ | >=2.1.0 | / |
| Rank | [FM](models/rank/fm/)
([doc](https://paddlerec.readthedocs.io/en/latest/models/rank/fm.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3240371) | ✓ | 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 | [BERT4REC](models/rank/bert4rec/) | - | ✓ | x | >=2.1.0 | [CIKM 2019][BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://arxiv.org/pdf/1904.06690.pdf) |
| Rank | [FAT_DeepFFM](models/rank/fat_deepffm/) | - | ✓ | x | >=2.1.0 | [2019][FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine](https://arxiv.org/pdf/1905.06336.pdf) |
| Rank | [FFM](models/rank/ffm/)
([doc](https://paddlerec.readthedocs.io/en/latest/models/rank/ffm.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3240369) | ✓ | 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/)
([doc](https://paddlerec.readthedocs.io/en/latest/models/rank/dcn.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3240207) | ✓ | 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/)
([doc](https://paddlerec.readthedocs.io/en/latest/models/rank/dmr.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3240346) | 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/)
([doc](https://paddlerec.readthedocs.io/en/latest/models/rank/deepfm.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238581) | ✓ | 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/)
([doc](https://paddlerec.readthedocs.io/en/latest/models/rank/xdeepfm.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3240533) | ✓ | 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/)
([doc](https://paddlerec.readthedocs.io/en/latest/models/rank/din.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3240307) | ✓ | 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/)
([doc](https://paddlerec.readthedocs.io/en/latest/models/rank/dien.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3240212) | ✓ | 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 | [GateNet](models/rank/gatenet/)
([doc](https://paddlerec.readthedocs.io/en/latest/models/rank/gatenet.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3240375) | ✓ | x | >=2.1.0 | [SIGIR 2020][GateNet: Gating-Enhanced Deep Network for Click-Through Rate Prediction](https://arxiv.org/pdf/2007.03519.pdf) |
| Rank | [DLRM](models/rank/dlrm/)
([doc](https://paddlerec.readthedocs.io/en/latest/models/rank/dlrm.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3240343) | ✓ | x | >=2.1.0 | [CoRR 2019][Deep Learning Recommendation Model for Personalization and Recommendation Systems](https://arxiv.org/abs/1906.00091) |
| Rank | [NAML](models/rank/naml/)
([doc](https://paddlerec.readthedocs.io/en/latest/models/rank/naml.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3240375) | ✓ | x | >=2.1.0 | [IJCAI 2019][Neural News Recommendation with Attentive Multi-View Learning](https://www.ijcai.org/proceedings/2019/0536.pdf) |
| Rank | [DIFM](models/rank/difm/)
([doc](https://paddlerec.readthedocs.io/en/latest/models/rank/difm.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3240286) | ✓ | x | >=2.1.0 | [IJCAI 2020][A Dual Input-aware Factorization Machine for CTR Prediction](https://www.ijcai.org/proceedings/2020/0434.pdf) |
| Rank | [DeepFEFM](models/rank/deepfefm/)
([doc](https://paddlerec.readthedocs.io/en/latest/models/rank/deepfefm.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3240209) | ✓ | x | >=2.1.0 | [arXiv 2020][Field-Embedded Factorization Machines for Click-through rate prediction](https://arxiv.org/abs/2009.09931) |
| Rank | [BST](models/rank/bst/)
([doc](https://paddlerec.readthedocs.io/en/latest/models/rank/bst.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3240205) | ✓ | x | >=2.1.0 | [DLP-KDD 2019][Behavior Sequence Transformer for E-commerce Recommendation in Alibaba](https://arxiv.org/pdf/1905.06874v1.pdf) |
| Rank | [AutoInt](models/rank/autoint) | - | ✓ | x | >=2.1.0 | [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/)
([doc](https://paddlerec.readthedocs.io/en/latest/models/rank/wide_deep.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238421) | ✓ | x | >=2.1.0 | [DLRS 2016][Wide & Deep Learning for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/2988450.2988454) |
| 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](models/rank/flen/) | - | ✓ | ✓ | >=2.1.0 | [2019][FLEN: Leveraging Field for Scalable CTR Prediction]( https://arxiv.org/pdf/1911.04690.pdf) |
| Rank | [DeepRec](models/rank/deeprec/) | - | ✓ | ✓ | >=2.1.0 | [2017][Training Deep AutoEncoders for Collaborative Filtering](https://arxiv.org/pdf/1708.01715v3.pdf) |
| Rank | [AutoFIS](models/rank/autofis/) | - | ✓ | ✓ | >=2.1.0 | [KDD 2020][AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction](https://arxiv.org/pdf/2003.11235v3.pdf) |
| Rank | [DCN_V2](models/rank/dcn_v2/) | - | ✓ | ✓ | >=2.1.0 | [WWW 2021][DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://arxiv.org/pdf/2008.13535v2.pdf)|
| Rank | [DSIN](models/rank/dsin/) | - | ✓ | ✓ | >=2.1.0 | [IJCAI 2019][Deep Session Interest Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1905.06482v1.pdf) |
| Rank | [SIGN](models/rank/sign/)([doc](https://paddlerec.readthedocs.io/en/latest/models/rank/sign.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3869111) | ✓ | ✓ | >=2.1.0 | [AAAI 2021][Detecting Beneficial Feature Interactions for Recommender Systems](https://arxiv.org/pdf/2008.00404v6.pdf) |
| Rank | [FGCNN](models/rank/fgcnn/)| - | ✓ | ✓ | >=2.1.0 | [WWW 2019][Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1904.04447.pdf) |
| Rank | [IPRec](models/rank/iprec/)([doc](https://paddl7erec.readthedocs.io/en/latest/models/rank/iprec.html)) | - | ✓ | ✓ | >=2.1.0 | [SIGIR 2021][Package Recommendation with Intra- and Inter-Package Attention Networks](http://nlp.csai.tsinghua.edu.cn/~xrb/publications/SIGIR-21_IPRec.pdf) |
| Rank | [DPIN](models/rank/dpin/)([doc](https://paddlerec.readthedocs.io/en/latest/models/rank/dpin.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/4419323) | ✓ | ✓ | >=2.1.0 | [SIGIR 2021][Deep Position-wise Interaction Network for CTR Prediction](https://arxiv.org/pdf/2106.05482v2.pdf) |
| Multi-Task | [AITM](models/rank/aitm/) | - | ✓ | ✓ | >=2.1.0 | [KDD 2021][Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising](https://arxiv.org/pdf/2105.08489v2.pdf) |
| Multi-Task | [PLE](models/multitask/ple/)
([doc](https://paddlerec.readthedocs.io/en/latest/models/multitask/ple.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238938) | ✓ | ✓ | >=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/)
([doc](https://paddlerec.readthedocs.io/en/latest/models/multitask/esmm.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238583) | ✓ | ✓ | >=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/)
([doc](https://paddlerec.readthedocs.io/en/latest/models/multitask/mmoe.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238934) | ✓ | ✓ | >=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/)
([doc](https://paddlerec.readthedocs.io/en/latest/models/multitask/share_bottom.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238943) | ✓ | ✓ | >=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/)
([doc](https://paddlerec.readthedocs.io/en/latest/models/multitask/maml.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238412) | x | x | >=2.1.0 | [PMLR 2017][Model-agnostic meta-learning for fast adaptation of deep networks](https://arxiv.org/pdf/1703.03400.pdf) |
| Multi-Task | [DSelect_K](models/multitask/dselect_k/)
([doc](https://paddlerec.readthedocs.io/en/latest/models/multitask/dselect_k.html)) | - | x | x | >=2.1.0 | [NeurIPS 2021][DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning](https://arxiv.org/pdf/2106.03760v3.pdf) |
| Multi-Task | [ESCM2](models/multitask/escm2/) | - | x | x | >=2.1.0 | [SIGIR 2022][ESCM2: Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation](https://arxiv.org/pdf/2204.05125.pdf) |
| Multi-Task | [MetaHeac](models/multitask/metaheac/) | - | x | x | >=2.1.0 | [KDD 2021][Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising](https://arxiv.org/pdf/2105.14688.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
### 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](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
PaddleRec QQ Group PaddleRec Wechat account