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This paper introduces a Biased Nonnegative Block Term (BNBT) tensor decomposition model for predicting dynamic Quality of Service (QoS) in cloud computing and Web services. BNBT enhances latent feature learning using block term tensor decomposition and incorporates linear bias terms to improve prediction accuracy. The model utilizes a tensor-oriented single-element-dependent nonnegative multiplicative update algorithm (SLF-NMUT) for efficient parameter estimation, demonstrating superior performance compared to existing QoS prediction methods on real-world datasets.
Overcome limitations in capturing complex user-service dependencies with a novel tensor decomposition method that significantly boosts QoS prediction accuracy.
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.
