\b{eta}-Divergence-Based Latent Factorization of Tensors model for QoS prediction

A nonnegative latent factorization of tensors (NLFT) model can well model the temporal pattern hidden in nonnegative 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 \b{eta}-divergence. Hence, can we build a generalized NLFT model via adopting \b{eta}-divergence to achieve prediction accuracy gain? To tackle this issue, this paper proposes a \b{eta}-divergence-based NLFT model (\b{eta}-NLFT). Its ideas are two-fold 1) building a learning objective with \b{eta}-divergence to achieve higher prediction accuracy, and 2) implementing self-adaptation of hyper-parameters to improve practicability. Empirical studies on two dynamic QoS datasets demonstrate that compared with state-of-the-art models, the proposed \b{eta}-NLFT model achieves the higher prediction accuracy for unobserved QoS data.