ABSTRACT An inherently non-negative latent factor model is proposed to extract non-negative latent factors from non-negative big sparse matrices efficiently and effectively. A single-element-dependent sigmoid function connects output latent factors with decision variables, such that non-negativity constraints on the output latent factors are always fulfilled and thus successfully separated from the training process with respect to the decision variables. Consequently, the proposed model can be easily and fast built with excellent prediction accuracy. Experimental results on an industrial size sparse matrix are given to verify its outstanding performance and suitability for industrial applications. INDEX TERMS Latent factors, non-negativity, matrix factorization, non-negative big sparse matrix, big data, recommender system.
Non-Negative latent factor models have been published in order to fulfil this constraint on the factors REF .
A Novel Approach to Extracting Non-Negative Latent Factors From Non-Negative Big Sparse Matrices
IEEE Access
IEEE Access
Computer Science