Covering-Based Web Service Quality Prediction via Neighborhood-Aware Matrix Factorization
Covering-Based Web Service Quality Prediction via Neighborhood-Aware Matrix Factorization
复制标题
通过邻域感知矩阵分解进行基于覆盖的 Web 服务质量预测
DOI：
10.1109/tsc.2019.2891517
复制
发表时间：
2021-09-01
影响因子：
8.1
通讯作者：
Yang, Yun
中科院分区：
文献类型：
--
作者：
Zhang, Yiwen;Wang, Kaibin;Yang, Yun
互联网上的Web服务数量一直在快速增长。这使得用户越来越难从大量功能相同的候选服务中找到合适的服务。检查每个Web服务的质量值是不切实际的，因为它非常消耗资源。因此，在过去的几年中，Web服务的质量预测问题引起了人们的极大关注，其中重点是矩阵分解(MF)技术的应用。最近，研究人员开始利用用户相似度来改进基于MF的Web服务预测方法。然而，现有的方法都没有正确和系统地解决两个主要问题：1)检索合适的邻域信息，即相似的用户和服务；2)利用完整的邻域信息，即用户和服务的邻域信息。本文通过邻域感知矩阵分解，提出了一种基于覆盖的Web服务质量预测方法CNMF。CNMF的新颖性有两个方面。首先，它使用基于覆盖的聚类方法来发现相似的用户和服务，该方法不需要预先指定簇的数目和簇的质心。其次，它利用用户和服务的邻域信息来提高预测精度。在包含1,974,675条Web服务调用记录的真实数据集上进行的实验结果表明，CNMF的性能明显优于现有的8种质量预测方法，其中包括两种也利用了MF的邻域信息的最新方法。
The number of Web services on the Internet has been growing rapidly. This has made it increasingly difficult for users to find the right services from a large number of functionally equivalent candidate services. Inspecting every Web service for their quality value is impractical because it is very resource consuming. Therefore, the problem of quality prediction for Web services has attracted a lot of attention in the past several years, with a focus on the application of the Matrix Factorization (MF) technique. Recently, researchers have started to employ user similarity to improve MF-based prediction methods for Web services. However, none of the existing methods has properly and systematically addressed two of the major issues: 1) retrieving appropriate neighborhood information, i.e., similar users and services; 2) utilizing full neighborhood information, i.e., both users’ and services’ neighborhood information. In this paper, we propose CNMF, a covering-based quality prediction method for Web services via neighborhood-aware matrix factorization. The novelty of CNMF is twofold. First, it employs a covering-based clustering method to find similar users and services, which does not require the number of clusters and cluster centroids to be prespecified. Second, it utilizes neighborhood information on both users and services to improve the prediction accuracy. The results of experiments conducted on a real-world dataset containing 1,974,675 Web service invocation records demonstrate that CNMF significantly outperforms eight existing quality prediction methods, including two state-of-the-art methods that also utilize neighborhood information with MF.