Title: FISM: Factored Item Similarity Models for Top-N Recommender Systems

Authors: Santosh Kabbur, Xia Ning, George Karypis

Abstract: The effectiveness of existing top-N recommendation methods decreases as the sparsity of the datasets increases. To alleviate this problem, we present an item-based method for generating top-N recommendations that learns the itemitem similarity matrix as the product of two low dimensional latent factor matrices. These matrices are learned using a structural equation modeling approach, wherein the value being estimated is not used for its own estimation. A comprehensive set of experiments on multiple datasets at three different sparsity levels indicate that the proposed methods can handle sparse datasets effectively and outperforms other state-of-the-art top-N recommendation methods. The experimental results also show that the relative performance gains compared to competing methods increase as the data gets sparser.

Running with RecBole

Model Hyper-Parameters:

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

  • split_to (int) : This is a parameter used to reduce the GPU memory usage during the evaluation. The larger the value, the less the memory usage and the slower the evaluation speed. Defaults to 0.

  • alpha (float) : It is a hyper-parameter controlling the normalization effect of the number of user history interactions when calculating the similarity. Defaults to 0.

  • reg_weights (list) : The L2 regularization weights. Defaults to [1e-2, 1e-2].

A Running Example:

Write the following code to a python file, such as

from recbole.quick_start import run_recbole

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

And then:


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]
reg_weights choice ['[1e-7, 1e-7]','[0, 0]']
alpha choice [0]
weight_size choice [64]
beta choice [0.5]

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 to tuning:

python --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