NFMF: neural fusion matrix factorisation for QoS prediction in service selection
NFMF: neural fusion matrix factorisation for QoS prediction in service selection
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NFMF：用于服务选择中 QoS 预测的神经融合矩阵分解
DOI：
10.1080/09540091.2021.1889975
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发表时间：
2021-02-20
影响因子：
5.3
通讯作者：
Liang, Wei
中科院分区：
文献类型：
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作者：
Xu, Jianlong;Xiao, Lijun;Liang, Wei
基于服务质量(Qos)选择合适的Web服务是开发高质量的面向服务的应用程序的关键。在这个方向上的一个关键步骤是获取准确的、个性化的Web服务的QOS值。由于Web服务数量庞大，服务质量数据高度稀疏，提高服务质量预测的准确性已成为一个具有挑战性的问题。在这项研究中，我们提出了一种新的服务质量预测模型，称为神经融合矩阵分解，其中我们结合神经网络和矩阵分解来对用户和服务的潜在特征向量进行非线性协同过滤。此外，我们还考虑了上下文偏差，并使用多任务学习来减少预测误差，提高预测性能。此外，我们在一个大规模的真实服务质量数据集上进行了大量的实验，实验结果验证了我们所提出的方法的有效性。
Selecting suitable web services based on the quality-of-service (QoS) is essential for developing high-quality service-oriented applications. A critical step in this direction is acquiring accurate, personalised QoS values of web services. As the number of web services is enormous and the QoS data are highly sparse, improving the accuracy of QoS prediction has become a challenging issue recently. In this study, we propose a novel QoS prediction model, called neural fusion matrix factorisation, wherein we combine neural networks and matrix factorisation to perform non-linear collaborative filtering for latent feature vectors of users and services. Moreover, we consider context bias and employ multi-task learning to reduce prediction error and improve the predicted performance. Furthermore, we conducted extensive experiments in a large-scale real-world QoS dataset, and the experimental results verify the effectiveness of our proposed method.