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yinhaofeng committed Jun 8, 2021
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Expand Up @@ -106,26 +106,26 @@ python -u tools/static_trainer.py -m models/rank/dnn/config.yaml # 静态图训
| 匹配 | [MultiView-Simnet](models/match/multiview-simnet/) | ✓ | ✓ | ✓ | x | 2.0 | [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) |
| 召回 | [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) |
| 召回 | [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) |
| 召回 | [MIND](https://github.com/PaddlePaddle/PaddleRec/tree/master/models/recall/mind) | ✓ | ✓ | x | x | 2.0 | [2019][Multi-Interest Network with Dynamic Routing for Recommendation at Tmall](https://arxiv.org/pdf/1904.08030.pdf) |
| 召回 | [MIND](models/recall/mind/) | ✓ | ✓ | x | x | 2.1 | [2019][Multi-Interest Network with Dynamic Routing for Recommendation at Tmall](https://arxiv.org/pdf/1904.08030.pdf) |
| 召回 | [Word2Vec](models/recall/word2vec/) | ✓ | ✓ | ✓ | x | 2.0 | [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) |
| 召回 | [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][Multtti-Rate Deep Learning for Temporal Recommendation](http://sonyis.me/paperpdf/spr209-song_sigir16.pdf) |
| 召回 | [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) |
| 召回 | [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) |
| 召回 | [NCF](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/recall/ncf/) | ✓ | ✓ | ✓ | ✓ | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [WWW 2017][Neural Collaborative Filtering](https://arxiv.org/pdf/1708.05031.pdf) |
| 召回 | [NCF](models/recall/ncf/) | ✓ | ✓ | ✓ | ✓ | 2.1 | [WWW 2017][Neural Collaborative Filtering](https://arxiv.org/pdf/1708.05031.pdf) |
| 召回 | [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) |
| 召回 | [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) |
| 排序 | [Logistic Regression](models/rank/logistic_regression/) | ✓ | ✓ | ✓ | x | 2.0 | / |
| 排序 | [Dnn](models/rank/dnn/) | ✓ | ✓ | ✓ | ✓ | 2.0 | / |
| 排序 | [FM](models/rank/fm/) | ✓ | ✓ | ✓ | x | 2.0 | [IEEE Data Mining 2010][Factorization machines](https://analyticsconsultores.com.mx/wp-content/uploads/2019/03/Factorization-Machines-Steffen-Rendle-Osaka-University-2010.pdf) |
| 排序 | [FFM](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/ffm/) | ✓ | ✓ | ✓ | x | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [RECSYS 2016][Field-aware Factorization Machines for CTR Prediction](https://dl.acm.org/doi/pdf/10.1145/2959100.2959134) |
| 排序 | [FFM](models/rank/ffm/) | ✓ | ✓ | ✓ | x | 2.1 | [RECSYS 2016][Field-aware Factorization Machines for CTR Prediction](https://dl.acm.org/doi/pdf/10.1145/2959100.2959134) |
| 排序 | [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) |
| 排序 | [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) |
| 排序 | [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) |
| 排序 | [DCN](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/dcn/) | ✓ | ✓ | ✓ | x | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [KDD 2017][Deep & Cross Network for Ad Click Predictions](https://dl.acm.org/doi/pdf/10.1145/3124749.3124754) |
| 排序 | [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) |
| 排序 | [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) |
| 排序 | [DeepFM](models/rank/deepfm/) | ✓ | ✓ | ✓ | x | 2.0 | [IJCAI 2017][DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://arxiv.org/pdf/1703.04247.pdf) |
| 排序 | [xDeepFM](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/xdeepfm) | ✓ | ✓ | ✓ | x | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [KDD 2018][xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3219819.3220023) |
| 排序 | [xDeepFM](models/rank/xdeepfm/) | ✓ | ✓ | ✓ | x | 2.1 | [KDD 2018][xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3219819.3220023) |
| 排序 | [DIN](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/din/) | ✓ | ✓ | ✓ | x | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [KDD 2018][Deep Interest Network for Click-Through Rate Prediction](https://dl.acm.org/doi/pdf/10.1145/3219819.3219823) |
| 排序 | [DIEN](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/dien/) | ✓ | ✓ | ✓ | x | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [AAAI 2019][Deep Interest Evolution Network for Click-Through Rate Prediction](https://www.aaai.org/ojs/index.php/AAAI/article/view/4545/4423) |
| 排序 | [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) |
Expand All @@ -134,10 +134,10 @@ python -u tools/static_trainer.py -m models/rank/dnn/config.yaml # 静态图训
| 排序 | [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) |
| 排序 | [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) |
| 排序 | [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) |
| 多任务 | [PLE](models/multitask/ple) | ✓ | ✓ | ✓ | ✓ | 2.0 | [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) |
| 多任务 | [PLE](models/multitask/ple/) | ✓ | ✓ | ✓ | ✓ | 2.1 | [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) |
| 多任务 | [ESMM](models/multitask/esmm/) | ✓ | ✓ | ✓ | ✓ | 2.0 | [SIGIR 2018][Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://arxiv.org/abs/1804.07931) |
| 多任务 | [MMOE](models/multitask/mmoe/) | ✓ | ✓ | ✓ | ✓ | 2.0 | [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) |
| 多任务 | [ShareBottom](models/multitask/share_bottom/) | ✓ | ✓ | ✓ | ✓ | 2.0 | [1998][Multitask learning](http://reports-archive.adm.cs.cmu.edu/anon/1997/CMU-CS-97-203.pdf) |
| 多任务 | [ShareBottom](models/multitask/share_bottom/) | ✓ | ✓ | ✓ | ✓ | 2.1 | [1998][Multitask learning](http://reports-archive.adm.cs.cmu.edu/anon/1997/CMU-CS-97-203.pdf) |
| 重排序 | [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) |


Expand All @@ -152,6 +152,7 @@ python -u tools/static_trainer.py -m models/rank/dnn/config.yaml # 静态图训
<p>

### 版本历史
- 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
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