KGGLM

Introduction

[paper]

Title: ….

Authors: …

Abstract: …

Running with hopwise

Model Hyper-Parameters:

  • embedding_size (int) : The embedding size of users, items, entities and relations. Defaults to 64.

  • loss_function (str) : The optimization loss function. Defaults to 'inner_product'. Range in ['inner_product', 'transe'].

  • margin (float) : The margin in margin loss, only be used when loss_function is set to 'transe'. Defaults to 1.0.

A Running Example:

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

from hopwise.quick_start import run_hopwise

run_hopwise(model='CFKG', 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]
loss_function choice ['inner_product', 'transe']
margin choice [0.5,1.0,2.0]

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 hopwise (you can download it from GitHub), you can run the run_hyper.py to tuning:

hopwise tune --config_files=[config_files_path] --params_file=hyper.test

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