Elastic net-regularized latent factor model for recommender systems

Latent factor (LF) models are highly efficient in recommender systems. The problem of LF analysis is defined on high-dimensional and sparse (HiDS) matrices corresponding to relationships among numerous entities in industrial applications. It is ill-posed without a unique and optimal solution. Hence, regularization schemes are vital in improving the generality of an LF model. This work innovatively applies the elastic net-based regularization to an LF model defined on HiDS matrices. To do so, we have 1) adapted the elastic net-based regularization scheme to an LF model to fit the sparsity of an HiDS matrix; and 2) designed feasible and efficient algorithm for an LF model with elastic net-based regularization. Experimental results on two large, real datasets show that with properly-tuned and elastic net-based regularization, the resultant model achieves 1) high prediction accuracy for missing data in an HiDS matrix; 2) high computational efficiency; and 3) sparse LF distribution.