Biased Block Term Tensor Decomposition for Temporal Pattern-aware QoS Prediction

The widespread application of cloud services make users pay more attention to Quality of Service (QoS). Generally, the user cannot call all services simultaneously to obtain corresponding QoS data and can only choose a service from a few known data, thus it’s critical to predict unknown QoS values. A third-order tensor can model temporal patterns of QoS data, and studies indicate that the tensor latent factor analysis models based on Canonical Polyadic (CP) decomposition can effectively capture temporal patterns to predict unknown data in QoS. However, the existing CP decomposition-based models limit their learning ability since rank-one tensors contain less structure information, which results in low prediction accuracy. Therefore, this paper proposes a Biased Block Term Tensor Decomposition (BBTTD) model to achieve high accuracy for temporal pattern-aware QoS prediction. It mainly adopts the following three-fold ideas: (a) implementing a tensor learning model by adopting the block term decomposition in rank-([Formula: see text], [Formula: see text], 1) terms; (b) proposing the bias block term tensors to enhance the model’s prediction accuracy; (c) designing a nonnegative multiplication update algorithm to learning model parameters. Extensive experiments on two public dynamic QoS datasets demonstrate that BBTTD has higher prediction accuracy compared with several QoS prediction models.