With the rapid development of services computing in the past decade, Quality-of-Service (QoS)-aware selection of Web services has become a hot yet thorny issue. Conducting warming-up tests on a large set of candidate services for QoS evaluation is time consuming and expensive, making it vital to implement accurate QoS-estimators. Existing QoS-estimators barely consider the temporal patterns hidden in QoS data. However, such data are naturally time dependent. For addressing this critical issue, this study presents a Kalman-filter-incorporated latent factor analysis (KLFA)-based QoS-estimator for accurate representation to temporally dynamic QoS data. Its main idea is to make the user latent features (LFs) time dependent, while the service ones time consistent. A novel iterative training scheme is designed, where the user LFs are learned through a Kalman filter for precisely modeling the temporal patterns, and the service ones are alternatively trained via an alternating least squares algorithm for precisely representing the historical QoS data. Empirical studies on large-scale and real Web service QoS datasets demonstrate that the proposed KLFA model significantly outperforms state-of-the-art QoS-estimators in estimation accuracy for dynamic QoS data.

中文翻译：

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结合卡尔曼滤波器的时态动态稀疏数据的潜在因子分析模型

随着服务计算在过去十年的快速发展，Web服务的服务质量（QoS）感知选择已成为一个热点而又棘手的问题。对大量候选服务进行预热测试以进行 QoS 评估既耗时又昂贵，因此实现准确的 QoS 估计器至关重要。现有的 QoS 估计器几乎没有考虑隐藏在 QoS 数据中的时间模式。然而，这些数据自然是依赖于时间的。为了解决这个关键问题，本研究提出了一种基于卡尔曼滤波器的潜在因子分析 (KLFA) 的 QoS 估计器，用于准确表示临时动态 QoS 数据。其主要思想是使用户潜在特征（LF）时间相关，而服务特征时间一致。设计了一种新颖的迭代训练方案，其中通过卡尔曼滤波器学习用户LF以精确建模时间模式，并且通过交替最小二乘算法交替训练服务LF以精确表示历史QoS数据。对大规模真实 Web 服务 QoS 数据集的实证研究表明，所提出的 KLFA 模型在动态 QoS 数据的估计精度方面显着优于最先进的 QoS 估计器。