Qingsheng Zhu

TL;DR: The idea is to investigate the non-negative update process depending on each involved feature rather than on the whole feature matrices, and propose the regularized single-element-based NMF (RSNMF) model, which is especially suitable for solving CF problems subject to the constraint of non-negativity.

TL;DR: An alternating direction method (ADM)-based nonnegative latent factor (ANLF) model is proposed, which ensures fast convergence and high prediction accuracy, as well as the maintenance of nonnegativity constraints.

TL;DR: Experimental results indicate that compared with the state-of-the-art predictors, the newly proposed one achieves significantly higher prediction accuracy at the expense of affordable computational burden, especially suitable for industrial applications requiring high prediction accuracy of unknown QoS data.

TL;DR: Comparison results between the proposed ensemble and several widely employed and state-of-the-art QoS predictors on two large, real data sets demonstrate that the former can outperform the latter well in terms of prediction accuracy.

TL;DR: This work aims to design an incremental CF recommender based on the Regularized Matrix Factorization (RMF), and first simplifies the training rule of RMF to propose the SI-RMF, which provides a simple mathematic form for further investigation.