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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
【PyTorch】Easy-to-use,Modular and Extendible package of deep-learning based CTR models.
| Date | Stars |
|---|---|
| 2026-07-31 | 3451 |
| 2026-08-03 | 3451 |
| 2026-08-06 | 3451 |
| 2026-08-07 | 3450 |
| 2026-08-09 | 3449 |
| 2026-08-10 | 3449 |
| 2026-08-11 | 3449 |
| 2026-08-12 | 3449 |
| 2026-08-15 | 3449 |
| 2026-08-16 | 3450 |
| 2026-08-18 | 3450 |
| 2026-08-28 | 3451 |
| 2026-08-29 | 3450 |
| 2026-08-30 | 3450 |
| 2026-08-31 | 3450 |
| 2026-09-01 | 3451 |
| 2026-09-02 | 3452 |
| 2026-09-03 | 3453 |
| 2026-09-05 | 3454 |
| 2026-09-09 | 3455 |
| 2026-09-11 | 3456 |
| 2026-09-12 | 3457 |
| 2026-09-13 | 3458 |
| 2026-09-14 | 3459 |
| 2026-09-16 | 3460 |
| 2026-09-18 | 3461 |
| 2026-09-19 | 3459 |
| 2026-09-20 | 3459 |
Today
— stars today
This week
+1 stars this week
This month
+9 stars this month
Momentum
0.0
growth rate 0.03%/day
# DeepCTR-Torch [](https://pypi.org/project/deepctr-torch) [](https://pepy.tech/project/deepctr-torch) [](https://pypi.org/project/deepctr-torch) [](https://github.com/shenweichen/deepctr-torch/issues) [](https://deepctr-torch.readthedocs.io/)  [](https://codecov.io/gh/shenweichen/DeepCTR-Torch) [](./README.md#disscussiongroup) [](https://github.com/shenweichen/deepctr-torch/blob/master/LICENSE) PyTorch version of [DeepCTR](https://github.com/shenweichen/DeepCTR). DeepCTR is a **Easy-to-use**,**Modular** and **Extendible** package of deep-learning based CTR models along with lots of core components layers which can be used to build your own custom model easily.You can use any complex model with `model.fit()`and `model.predict()` .Install through `pip install -U deepctr-torch`. Let's [**Get Started!**](https://deepctr-torch.readthedocs.io/en/latest/Quick-Start.html)([Chinese Introduction](https://zhuanlan.zhihu.com/p/53231955)) ## Models List | Model | Paper | | :------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Convolutional Click Prediction Model | [CIKM 2015][A Convolutional Click Prediction Model](http://ir.ia.ac.cn/bitstream/173211/12337/1/A%20Convolutional%20Click%20Prediction%20Model.pdf) | | Factorization-supported Neural Network | [ECIR 2016][Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction](https://arxiv.org/pdf/1601.02376.pdf) | | Product-based Neural Network | [ICDM 2016][Product-based neural networks for user response prediction](https://arxiv.org/pdf/1611.00144.pdf) | | Wide & Deep | [DLRS 2016][Wide & Deep Learning for Recommender Systems](https://arxiv.org/pdf/1606.07792.pdf) | | DeepFM | [IJCAI 2017][DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](http://www.ijcai.org/proceedings/2017/0239.pdf) | | Piece-wise Linear Model | [arxiv 2017][Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction](https://arxiv.org/abs/1704.05194) | | Deep & Cross Network | [ADKDD 2017][Deep & Cross Network for Ad Click Predictions](https://arxiv.org/abs/1708.05123) | | Attentional Factorization Machine | [IJCAI 2017][Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks](http://www.ijcai.org/proceedings/2017/435) | | Neural Factorization Machine | [SIGIR 2017][Neural Factorization Machines for Sparse Predictive Analytics](https://arxiv.org/pdf/1708.05027.pdf) | | xDeepFM | [KDD 2018][xDeepFM: Comb
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:ab54c0e897926664, topic:deep-learning