Non-Negative Latent Factorization of Tensors Model Based on $\beta$-Divergence for Time-Aware QoS Prediction

A non-negative latent factorization of tensors (NLFT) model can well model the temporal pattern hidden in non-negative quality-of-service (QoS) data for predicting the unobserved ones with high accuracy. However, existing NLFT models' objective function is based on Euclidean distance, which is only a special case of $\beta$-divergence. Hence, can we build a generalized NLFT model via adopting $\beta$-divergence to achieve prediction accuracy gain? To tackle this issue, this paper proposes a $\beta$-divergence-based NLFT model ($\beta$-NLFT). Its ideas are two-fold: 1) building a learning objective with $\beta$-divergence to achieve higher prediction accuracy; and 2) implementing self-adaptation of hyper-parameters to improve practicability. Experimental results generated from two dynamic QoS datasets show that the proposed $\beta$-NLFT model can achieve the higher prediction accuracy than state-of-the-art models several when predicting the unobserved QoS data.