Quality-of-service (QoS) data exhibit dynamic temporal patterns that are crucial for accurately predicting missing values. These patterns arise from the evolving interactions between users and services, making it essential to capture the temporal dynamics inherent in such data for improved prediction performance. As the size and complexity of QoS datasets increase, existing models struggle to provide accurate predictions, highlighting the need for more flexible and dynamic methods to better capture the underlying patterns in large-scale QoS data. To address this issue, we introduce a neural network-based tensor factorization approach tailored for learning spatiotemporal representations of high-dimensional and incomplete (HDI) tensors, namely the Multi-head Self-attending Neural Tucker Factorization (MSNTucF). The model is elaborately designed for modeling intricate nonlinear spatiotemporal feature interaction patterns hidden in real world data with a two-fold idea. It first employs a neural network structure to generalize the traditional framework of Tucker factorization and then proposes to leverage a multi-head self-attending module to enforce nonlinear latent interaction learning. In empirical studies on two dynamic QoS datasets from real applications, the proposed MSNTucF model demonstrates superior performance compared to state-of-the-art benchmark models in estimating missing observations. This highlights its ability to learn non-linear spatiotemporal representations of HDI tensors.

翻译：服务质量（QoS）数据展现出动态的时间模式，这对于准确预测缺失值至关重要。这些模式源于用户与服务之间不断演化的交互，因此捕捉此类数据中固有的时间动态对于提升预测性能至关重要。随着QoS数据集规模和复杂性的增加，现有模型难以提供准确的预测，这突显了对更灵活、动态的方法的需求，以更好地捕捉大规模QoS数据中的潜在模式。为解决这一问题，我们提出了一种基于神经网络的张量分解方法，专为学习高维不完整（HDI）张量的时空表示而设计，即多头自注意力神经塔克分解（MSNTucF）。该模型精心设计，用于建模现实世界数据中隐藏的复杂非线性时空特征交互模式，其核心思想包含两个方面。它首先采用神经网络结构来推广传统的塔克分解框架，然后提出利用多头自注意力模块来强化非线性潜在交互学习。在两个来自实际应用的动态QoS数据集上的实证研究中，所提出的MSNTucF模型在估计缺失观测值方面相比最先进的基准模型展现出优越性能。这突显了其学习HDI张量非线性时空表示的能力。