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e0d38aeb
编写于
6月 15, 2020
作者:
Y
yaoxuefeng
提交者：
GitHub
6月 15, 2020
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README.md
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| 召回 | [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) |
| 召回 | [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) |
| 召回 | [NCF](models/recall/ncf/model.py) | ✓ | ✓ | ✓ | ✓ | [WWW 2017][Neural Collaborative Filtering](https://arxiv.org/pdf/1708.05031.pdf) |
| 召回 | [NCF](models/recall/ncf/model.py) | ✓ | ✓ | ✓ | ✓ | [WWW 2017][Neural Collaborative Filtering](https://arxiv.org/pdf/1708.05031.pdf) |
| 召回 | [GNN](models/recall/gnn/model.py) | ✓ | ✓ | ✓ | ✓ | [AAAI 2019][Session-based Recommendation with Graph Neural Networks](https://arxiv.org/abs/1811.00855) |
| 召回 | [GNN](models/recall/gnn/model.py) | ✓ | ✓ | ✓ | ✓ | [AAAI 2019][Session-based Recommendation with Graph Neural Networks](https://arxiv.org/abs/1811.00855) |
| 排序 | [Logistic Regression](models/rank/logistic_regression/model.py) | ✓ | ✓ | ✓ | x | / |
| 排序 | [Logistic Regression](models/rank/logistic_regression/model.py) | ✓ | x | ✓ | x | / |
| 排序 | [Dnn](models/rank/dnn/model.py) | ✓ | ✓ | ✓ | ✓ | / |
| 排序 | [Dnn](models/rank/dnn/model.py) | ✓ | ✓ | ✓ | ✓ | / |
| 排序 | [FM](models/rank/fm/model.py) | ✓ | ✓ | ✓ | ✓ | / |
| 排序 | [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) |
| 排序 | [FFM](models/rank/ffm/model.py) | ✓ | ✓ | ✓ | x | / |
| 排序 | [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) |
| 排序 | [Pnn](models/rank/pnn/model.py) | >=2.0 | >=2.0 | >=2.0 | >=2.0 | / |
| 排序 | [FNN](models/rank/fnn/model.py) | ✓ | x | ✓ | x | [ECIR 2016][Deep Learning over Multi-field Categorical Data](https://arxiv.org/pdf/1601.02376.pdf) |
| 排序 | [DCN](models/rank/dcn/model.py) | ✓ | ✓ | ✓ | x | / |
| 排序 | [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) |
| 排序 | [NFM](models/rank/nfm/model.py) | ✓ | ✓ | ✓ | x | / |
| 排序 | [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) |
| 排序 | [AFM](models/rank/afm/model.py) | ✓ | ✓ | ✓ | x | / |
| 排序 | [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) |
| 排序 | [DeepFM](models/rank/deepfm/model.py) | ✓ | ✓ | ✓ | x | / |
| 排序 | [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) |
| 排序 | [xDeepFM](models/rank/xdeepfm/model.py) | ✓ | ✓ | ✓ | x | / |
| 排序 | [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) |
| 排序 | [DIN](models/rank/din/model.py) | ✓ | ✓ | ✓ | x | / |
| 排序 | [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) |
| 排序 | [Wide&Deep](models/rank/wide_deep/model.py) | ✓ | ✓ | ✓ | x | / |
| 排序 | [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) |
| 排序 | [FGCNN](models/rank/fgcnn/model.py) | ✓ | ✓ | ✓ | ✓ | / |
| 排序 | [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) |
| 排序 | [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) |
| 排序 | [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)|
| 多任务 | [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) |
| 多任务 | [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) |
| 多任务 | [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) |
| 多任务 | [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) |
| 多任务 | [ShareBottom](models/multitask/share-bottom/model.py) | ✓ | ✓ | ✓ | ✓ | [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/model.py) | ✓ | ✓ | ✓ | ✓ | [1998][Multitask learning](http://reports-archive.adm.cs.cmu.edu/anon/1997/CMU-CS-97-203.pdf) |
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models/rank/readme.md
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| Logistic Regression | 逻辑回归 | -- |
| Logistic Regression | 逻辑回归 | -- |
| FM | 因子分解机 |
[
Factorization Machine
](
https://ieeexplore.ieee.org/abstract/document/5694074
)(
2010
)
|
| FM | 因子分解机 |
[
Factorization Machine
](
https://ieeexplore.ieee.org/abstract/document/5694074
)(
2010
)
|
| FFM | Field-Aware FM |
[
Field-aware Factorization Machines for CTR Prediction
](
https://dl.acm.org/doi/pdf/10.1145/2959100.2959134
)(
2016
)
|
| FFM | Field-Aware FM |
[
Field-aware Factorization Machines for CTR Prediction
](
https://dl.acm.org/doi/pdf/10.1145/2959100.2959134
)(
2016
)
|
| FNN | Factorisation-Machine Supported Neural Networks |
[
Deep Learning over Multi-field Categorical Data
](
https://arxiv.org/pdf/1601.02376.pdf
)(
2016
)
|
| Deep Crossing | Deep Crossing |
[
Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features
](
https://www.kdd.org/kdd2016/papers/files/adf0975-shanA.pdf
)(
2016
)
|
| PNN | Product Network |
[
Product-based Neural Networks for User Response Prediction
](
https://arxiv.org/pdf/1611.00144.pdf
)(
2016
)
|
| PNN | Product Network |
[
Product-based Neural Networks for User Response Prediction
](
https://arxiv.org/pdf/1611.00144.pdf
)(
2016
)
|
| wide&deep | Deep + wide(LR) |
[
Wide & Deep Learning for Recommender Systems
](
https://dl.acm.org/doi/pdf/10.1145/2988450.2988454
)(
2016
)
|
| wide&deep | Deep + wide(LR) |
[
Wide & Deep Learning for Recommender Systems
](
https://dl.acm.org/doi/pdf/10.1145/2988450.2988454
)(
2016
)
|
| DeepFM | DeepFM |
[
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
](
https://arxiv.org/pdf/1703.04247.pdf
)(
2017
)
|
| DeepFM | DeepFM |
[
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
](
https://arxiv.org/pdf/1703.04247.pdf
)(
2017
)
|
...
...
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