$β$-Divergence-Based Latent Factorization of Tensors model for QoS prediction
This work addresses QoS prediction for service-oriented systems, but it is incremental as it generalizes an existing model by replacing Euclidean distance with β-divergence.
The paper tackles the problem of predicting unobserved quality-of-service (QoS) data by proposing a β-divergence-based nonnegative latent factorization of tensors model, achieving higher prediction accuracy compared to state-of-the-art models on two dynamic QoS datasets.
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 $β$-divergence. Hence, can we build a generalized NLFT model via adopting $β$-divergence to achieve prediction accuracy gain? To tackle this issue, this paper proposes a $β$-divergence-based NLFT model ($β$-NLFT). Its ideas are two-fold 1) building a learning objective with $β$-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 $β$-NLFT model achieves the higher prediction accuracy for unobserved QoS data.