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¶
embedding_size (int): The embedding size of users and items. Defaults to
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
alpha (float): It is a hyper-parameter controlling the normalization effect of the number of user history interactions when calculating the similarity. Defaults to
reg_weights (list): The L2 regularization weights. Defaults to
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='FISM', dataset='ml-100k')
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
learning_rate choice [0.01,0.005,0.001,0.0005,0.0001] reg_weights choice ['[1e-7, 1e-7]','[0, 0]'] alpha choice  weight_size choice  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
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