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An Efficient Non-Negative Matrix-Factorization-Based Approach to Collaborative Filtering for Recommender Systems
TLDR
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.
A Nonnegative Latent Factor Model for Large-Scale Sparse Matrices in Recommender Systems via Alternating Direction Method
TLDR
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.
Generating Highly Accurate Predictions for Missing QoS Data via Aggregating Nonnegative Latent Factor Models
TLDR
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.
Incorporation of Efficient Second-Order Solvers Into Latent Factor Models for Accurate Prediction of Missing QoS Data
TLDR
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.
Deadline-Constrained Cost Optimization Approaches for Workflow Scheduling in Clouds
TLDR
Experimental results show that compared with traditional algorithms, the performance of ProLiS is very competitive and L-ACO performs the best in terms of execution costs and success ratios of meeting deadlines.
An Efficient Second-Order Approach to Factorize Sparse Matrices in Recommender Systems
TLDR
This work proposes the Hessian-free optimization-based LF model, which is able to extract latent factors from the given incomplete matrices via a second-order optimization process, and is a promising model for implementing high-performance recommenders.
A Highly Efficient Approach to Protein Interactome Mapping Based on Collaborative Filtering Framework
TLDR
This work proposes a CF framework for binary interactome mapping, and designs the rescaled cosine coefficient to model the inter-neighborhood similarity among involved proteins, for taking the mapping process.
An Incremental-and-Static-Combined Scheme for Matrix-Factorization-Based Collaborative Filtering
TLDR
This work aims to develop a general, incremental- and-static-combined scheme for MF-based CF to obtain highly accurate and computationally affordable incremental recommenders.
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