The minimum PaddlePaddle version needed for the code sample in this directory is v0.11.0. If you are on a version of PaddlePaddle earlier than v0.11.0, [please update your installation](http://www.paddlepaddle.org/docs/develop/documentation/en/build_and_install/pip_install_en.html).
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# DNN for Click-Through Rate prediction
# Deep Factorization Machine for Click-Through Rate prediction
## Introduction
## Introduction
This model implements the DeepFM proposed in the following paper:
This model implements the DNN part proposed in the following paper:
```text
```text
@inproceedings{guo2017deepfm,
@inproceedings{guo2017deepfm,
...
@@ -38,25 +35,9 @@ cd data && ./download.sh && cd ..
...
@@ -38,25 +35,9 @@ cd data && ./download.sh && cd ..
```
```
## Model
## Model
The DeepFM model is composed of the factorization machine layer (FM) and deep
This Demo only implement the DNN part of the model described in DeepFM paper.
neural networks (DNN). All the input features are feeded to both FM and DNN.
DeepFM model will be provided in other model.
The output from FM and DNN are combined to form the final output. The embedding
layer for sparse features in the DNN shares the parameters with the latent
vectors (factors) of the FM layer.
The factorization machine layer in PaddlePaddle computes the second order
interactions. The following code example combines the factorization machine
layer and fully connected layer to form the full version of factorization