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 easily build custom models.You can use any complex model with model.fit() ，and model.predict() .

• Provide tf.keras.Model like interfaces for quick experiment. example
• Provide tensorflow estimator interface for large scale data and distributed training. example
• It is compatible with both tf 1.15 and tf 2.x.

Installation and compatibility

DeepCTR does not pin or install TensorFlow for you. Install a TensorFlow build that matches your Python, NumPy, CPU/GPU, and operating system first, then install DeepCTR:

pip install tensorflow
pip install deepctr

For Python >=3.9, DeepCTR allows modern h5py releases with h5py>=3.7.0. If TensorFlow reports a NumPy conflict, follow the TensorFlow requirement for your selected TensorFlow release, for example using numpy<2 when required by TensorFlow.

Use public tensorflow.keras APIs in your own code and examples. Avoid mixing tensorflow.python.keras with tensorflow.keras, because tensorflow.python.* is private TensorFlow API and can break model serialization or optimizer/metric loading across TensorFlow versions.

Some related projects:

• DeepMatch: https://github.com/shenweichen/DeepMatch
• DeepCTR-Torch: https://github.com/shenweichen/DeepCTR-Torch

Let's Get Started!(Chinese Introduction) and welcome to join us!

Models List

Citation

• Weichen Shen. (2017). DeepCTR: Easy-to-use,Modular and Extendible package of deep-learning based CTR models. https://github.com/shenweichen/deepctr.

If you find this code useful in your research, please cite it using the following BibTeX:

@misc{shen2017deepctr,
  author = {Weichen Shen},
  title = {DeepCTR: Easy-to-use,Modular and Extendible package of deep-learning based CTR models},
  year = {2017},
  publisher = {GitHub},
  journal = {GitHub Repository},
  howpublished = {\url{https://github.com/shenweichen/deepctr}},
}

DisscussionGroup

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