An ADMM-Incorporated Latent Factorization of Tensors Method for QoS Prediction
This work addresses incremental improvements in QoS prediction for web service selection, focusing on convergence speed and robustness to outliers.
The paper tackles the problem of slow convergence and outlier sensitivity in latent factorization of tensors for QoS prediction by proposing an ADMM-incorporated outlier-resilient nonnegative model, achieving faster convergence and improved prediction accuracy on two dynamic QoS datasets.
As the Internet developed rapidly, it is important to choose suitable web services from a wide range of candidates. Quality of service (QoS) describes the performance of a web service dynamically with respect to the service requested by the service consumer. Moreover, the latent factorization of tenors (LFT) is very effective for discovering temporal patterns in high dimensional and sparse (HiDS) tensors. However, current LFT models suffer from a low convergence rate and rarely account for the effects of outliers. To address the above problems, this paper proposes an Alternating direction method of multipliers (ADMM)-based Outlier-Resilient Nonnegative Latent-factorization of Tensors model. We maintain the non-negativity of the model by constructing an augmented Lagrangian function with the ADMM optimization framework. In addition, the Cauchy function is taken as the metric function to reduce the impact on the model training. The empirical work on two dynamic QoS datasets shows that the proposed method has faster convergence and better performance on prediction accuracy.