# LR¶

## Introduction¶

[paper]

Title: Predicting Clicks Estimating the Click-Through Rate for New Ads

Authors: Matthew Richardson, Ewa Dominowska, Robert Ragno

Model Hyper-Parameters:

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

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='LR', 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]


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