  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.
