Quality of service (QoS) has been mostly applied to represent non-functional properties of Web services and differentiate those with the same functionality. How to accurately predict service QoS has become a key research topic. Researchers have employed neighborhood information into matrix factorization (MF) for service QoS prediction in recent years. However, they are restricted to traditional matrix factorization that may incur a couple of limitations. 1) Conventional MF for QoS prediction linearly combines the multiplication of the latent feature representation of users and services through inner product, failing to fully capture the implicit features of user and service. 2) Most of approaches integrate user or service neighborhood as heuristics into MF model, where either location context or historical invocation records are used to calculate similar users or services. Nevertheless, combining both of them together in a collaborative way is ignored for neighborhood selection that has yet to be properly explored. To deal with the challenges, we propose a novel approach for service QoS prediction called ${N}$ eighborhood-integrated ${D}$ eep ${M}$ atrix ${F}$ actorization (NDMF), which integrates user neighborhood selected by a collaborative way into an enhanced matrix factorization model via deep neural network (DNN). We implement a prototype system and conduct extensive experiments on public and real-world large Web service dataset with almost 2,000,000 service invocations called WS-DREAM which is widely used in service QoS prediction. The experimental results demonstrate that our proposed approach significantly outperforms state-of-the-art ones in terms of multiple evaluation metrics.

中文翻译：

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NDMF：用于服务 QoS 预测的邻域集成​​深度矩阵分解

服务质量 (QoS) 主要用于表示 Web 服务的非功能特性并区分具有相同功能的那些。如何准确预测服务QoS已成为一个重要的研究课题。近年来，研究人员已将邻域信息应用于矩阵分解 (MF) 以进行服务 QoS 预测。但是，它们仅限于传统的矩阵分解，这可能会带来一些限制。1）用于QoS预测的传统MF通过内积线性组合用户和服务的潜在特征表示的乘法，未能完全捕获用户和服务的隐含特征。2）大多数方法将用户或服务邻域作为启发式集成到 MF 模型中，其中位置上下文或历史调用记录用于计算相似的用户或服务。然而，对于尚未正确探索的邻域选择，以协作方式将它们结合在一起被忽略。为了应对这些挑战，我们提出了一种新的服务 QoS 预测方法，称为 ${N}$ 社区综合 ${D}$ 伊普 ${M}$ 矩阵 ${F}$ 动作化（NDMF），它通过深度神经网络（DNN）将协作方式选择的用户邻域集成到增强的矩阵分解模型中。我们实现了一个原型系统，并对公共和现实世界的大型 Web 服务数据集进行了广泛的实验，该数据集具有近 2,000,000 次服务调用，称为 WS-DREAM，广泛用于服务 QoS 预测。实验结果表明，我们提出的方法在多个评估指标方面明显优于最先进的方法。