$\beta$-Divergence-Based Latent Factorization of Tensors model for QoS prediction

TL;DR

This paper introduces a $eta$-divergence-based nonnegative latent tensor factorization model for QoS prediction, improving accuracy by generalizing the objective function and adapting hyper-parameters.

Contribution

It proposes a novel $eta$-NLFT model that extends existing Euclidean-based models using $eta$-divergence and incorporates self-adaptive hyper-parameter tuning.

Findings

$eta$-NLFT outperforms state-of-the-art models in prediction accuracy.

The model effectively captures temporal patterns in QoS data.

Self-adaptive hyper-parameter tuning enhances model practicality.

Abstract

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 $\beta$-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. Empirical studies on two dynamic QoS datasets demonstrate that compared with state-of-the-art models, the proposed $\beta$-NLFT model achieves the higher prediction accuracy for unobserved QoS data.