FFM¶

Introduction¶

[paper]

Title: Field-aware Factorization Machines for CTR Prediction

Authors: Yuchin Juan, Yong Zhuang, Wei-Sheng Chin, Chih-Jen Lin

Abstract: Click-through rate (CTR) prediction plays an important role in computational advertising. Models based on degree-2 polynomial mappings and factorization machines (FMs) are widely used for this task. Recently, a variant of FMs, field-aware factorization machines (FFMs), outperforms existing models in some world-wide CTR-prediction competitions. Based on our experiences in winning two of them, in this paper we establish FFMs as an effective method for classifying large sparse data including those from CTR prediction. First, we propose efficient implementations for training FFMs. Then we comprehensively analyze FFMs and compare this approach with competing models. Experiments show that FFMs are very useful for certain classification problems. Finally, we have released a package of FFMs for public use.

Model Hyper-Parameters:

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

• fields (dict) : This parameter defines the mapping from fields to features, key is field’s id, value is a list of features in this field. For example, in ml-100k dataset, it can be set as {0: ['user_id','age'], 1: ['item_id', 'class']}. If it is set to None, the features and the fields are corresponding one-to-one. Defaults to None.

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='FFM', dataset='ml-100k')


And then:

python run.py


Notes:

• The features defined in fields must be in the dataset and be loaded by data module in RecBole. It means the value in fields must appear in load_col.

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