Efficient Extraction of Non-negative Latent Factors from High-Dimensional and Sparse Matrices in Industrial Applications

High-dimensional and sparse (HiDS) matrices are commonly encountered in many big data-related industrial applications like recommender systems. When acquiring useful patterns from them, non-negative matrix factorization (NMF) models have proven to be highly effective because of their fine representativeness of non-negative data. However, current NMF techniques suffer from a) inefficiency in addressing HiDS matrices, and b) constrained training schemes lack of flexibility, extensibility and adaptability. To address these issues, this work proposes to factorize industrial-size sparse matrices via a novel Inherently Non-negative Latent Factor (INLF) model. It connects the output factors and decision variables via a single-element-dependent sigmoid function, thereby innovatively removing the non-negativity constraints from its training process without impacting the solution accuracy. Hence, its training process is unconstrained, highly flexible and compatible with general learning schemes. Experimental results on five HiDS matrices generated by industrial applications indicate that INLF is able to acquire non-negative latent factors from them in a more efficient manner than any existing method does.