论文标题
$β$ - 基于QOS预测的张量模型的潜在分解
$β$-Divergence-Based Latent Factorization of Tensors model for QoS prediction
论文作者
论文摘要
张量（NLFT）模型的非负潜在分解可以很好地模拟隐藏在非负服务质量（QOS）数据中的时间模式，以预测具有高精度的未观察到的时间模式。但是，现有的NLFT模型的目标函数基于欧几里得距离，这只是$β$ divergence的特殊情况。因此，我们可以通过采用$β$ - 差异来实现预测准确性增长来构建广义的NLFT模型吗？为了解决这个问题，本文提出了一个基于$β$ -Divergence的NLFT模型（$β$ -NLFT）。它的想法是双重的1）建立一个以$β$ - 差异来实现更高预测准确性的学习目标，以及2）实施对超参数的自适应以提高实用性。对两个动态QoS数据集的实证研究表明，与最先进的模型相比，提出的$β$ -NLFT模型可实现未观察到的QoS数据的较高预测准确性。
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.
