# DeepFM¶

## Introduction¶

[paper]

Title: DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

Authors: Huifeng Guo , Ruiming Tang, Yunming Yey, Zhenguo Li, Xiuqiang He

Abstract: Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems. Despite great progress, existing methods seem to have a strong bias towards low- or high-order interactions, or require expertise feature engineering. In this paper, we show that it is possible to derive an end-to-end learning model that emphasizes both low- and high-order feature interactions. The proposed model, DeepFM, combines the power of factorization machines for recommendation and deep learning for feature learning in a new neural network architecture. Compared to the latest Wide & Deep model from Google, DeepFM has a shared input to its “wide” and “deep” parts, with no need of feature engineering besides raw features. Comprehensive experiments are conducted to demonstrate the effectiveness and efficiency of DeepFM over the existing models for CTR prediction, on both benchmark data and commercial data.

Model Hyper-Parameters:

• embedding_size (int) : The embedding size of features. Defaults to 10.

• mlp_hidden_size (list of int) : The hidden size of MLP layers. Defaults to [128,128,128].

• dropout_prob (float) : The dropout rate. Defaults to 0.2.

A Running Example:

Write the following code to a python file, such as run.py

from recbole.quick_start import run_recbole

run_recbole(model='DeepFM', dataset='ml-100k')


And then:

python run.py


## Tuning Hyper Parameters¶

If you want to use HyperTuning to tune hyper parameters of this model, you can copy the following settings and name it as hyper.test.

learning_rate choice [0.01,0.005,0.001,0.0005,0.0001]
dropout_prob choice [0.0,0.1,0.2,0.3,0.4,0.5]
mlp_hidden_size choice ['[64,64,64]','[128,128,128]','[256,256,256]','[512,512,512]']


Note that we just provide these hyper parameter ranges for reference only, and we can not guarantee that they are the optimal range of this model.

Then, with the source code of RecBole (you can download it from GitHub), you can run the run_hyper.py to tuning:

python run_hyper.py --model=[model_name] --dataset=[dataset_name] --config_files=[config_files_path] --params_file=hyper.test


For more details about Parameter Tuning, refer to Parameter Tuning.

If you want to change parameters, dataset or evaluation settings, take a look at