Title: ADMM SLIM: Sparse Recommendations for Many Users
Authors: Harald Steck,Maria Dimakopoulou,Nickolai Riabov,Tony Jebara
Abstract: The Sparse Linear Method (Slim) is a well-established approach for top-N recommendations. This article proposes several improvements that are enabled by the Alternating Directions Method of Multipliers (ADMM), a well-known optimization method with many application areas. First, we show that optimizing the original Slim-objective by ADMM results in an approach where the training time is independent of the number of users in the training data, and hence trivially scales to large numbers of users. Second, the flexibility of ADMM allows us to switch on and off the various constraints and regularization terms in the original Slim-objective, in order to empirically assess their contributions to ranking accuracy on given data. Third, we also propose two extensions to the original Slim training-objective in order to improve recommendation accuracy further without increasing the computational cost. In our experiments on three well-known data-sets, we first compare to the original Slim-implementation and find that not only ADMM reduces training time considerably, but also achieves an improvement in recommendation accuracy due to better optimization. We then compare to various state-of-the-art approaches and observe up to 25% improvement in recommendation accuracy in our experiments. Finally, we evaluate the importance of sparsity and the non-negativity constraint in the original Slim-objective with subsampling experiments that simulate scenarios of cold-starting and large catalog sizes compared to relatively small user base, which often occur in practice.
Running with RecBole¶
lambda1 (float): L1-norm regularization parameter. Defaults to
lambda2 (float): L2-norm regularization parameter. Defaults to
alpha (float): The exponents to control the power-law in the regularization terms. Defaults to
rho (float): The penalty parameter that applies to the squared difference between primal variables. Defaults to
k (int): The number of running iterations. Defaults to
positive_only (bool): Whether or not to preserve all positive values only. Defaults to
center_columns (bool): Whether or not to use additional item-bias terms. 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='ADMMSLIM', 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
lambda1 choice [0.1 , 0.5 , 5 , 10] lambda2 choice [5 , 50 , 1000 , 5000] alpha choice [0.25 , 0.5 , 0.75 , 1]
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