With the rapid development of cloud computing and Web services, Quality of Service (QoS) has become a key criterion for service selection and recommendation. Tensor latent feature analysis provides an effective way to model multidimensional QoS data, and most existing QoS prediction methods are mainly based on Canonical Polyadic (CP) decomposition or Tucker decomposition. However, constrained by their inherent structural properties, these methods cannot accurately capture the complex and dynamic dependencies in user-service interactions, which limits their prediction performance. To address this issue, this paper proposes a dynamic QoS prediction framework based on the Biased Nonnegative Block Term Tensor Decomposition Model, termed BNBT. Specifically, the proposed framework is developed from three aspects: (1) block term tensor decomposition is employed to enhance the representation capability of latent feature learning; (2) linear bias terms are incorporated to further improve prediction accuracy; and (3) a tensor-oriented single-element-dependent nonnegative multiplicative update algorithm, called SLF-NMUT, is designed for efficient parameter estimation. Extensive experiments on real-world QoS datasets demonstrate that the proposed BNBT framework consistently outperforms several state-of-the-art QoS prediction methods in terms of prediction accuracy.

翻译：随着云计算和Web服务的快速发展，服务质量（Quality of Service, QoS）已成为服务选择与推荐的关键准则。张量潜在特征分析为建模多维QoS数据提供了有效途径，现有QoS预测方法主要基于规范多元分解（Canonical Polyadic, CP）或Tucker分解。然而，受限于自身结构特性，这些方法无法准确捕捉用户-服务交互中复杂且动态的依赖关系，从而制约了其预测性能。为解决该问题，本文提出一种基于有偏非负块项张量分解模型的动态QoS预测框架，记为BNBT。具体而言，该框架从三方面构建：（1）采用块项张量分解以增强潜在特征学习的表示能力；（2）引入线性偏置项以进一步提升预测精度；（3）设计面向张量的单元素依赖非负乘性更新算法（SLF-NMUT）以实现高效参数估计。在真实QoS数据集上的大量实验表明，所提出的BNBT框架在预测精度上始终优于多种先进的QoS预测方法。