Cloud manufacturing service QoS prediction based on neighbourhood enhanced matrix factorization
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Cloud manufacturing service QoS prediction based on neighbourhood enhanced matrix factorization Yu Feng1 · Biqing Huang1 Received: 28 December 2017 / Accepted: 2 March 2018 © Springer Science+Business Media, LLC, part of Springer Nature 2018
Abstract With the rapid development of cloud manufacturing (CMfg), quality-of-service (QoS) prediction becomes increasingly important in CMfg service platform because it turns out to be impractical to acquire all service QoS values. In this paper, we present a neighbourhood enhanced matrix factorization approach to predict missing QoS values. We first systematically consider geographical information, sample set diversity computation and platform context to extend basic Pearson Correlation Coefficient (PCC) similarity and extract neighbourhood information. Then, we integrate neighbourhood information into matrix factorization (MF) and make prediction of missing values. Compared with existing methods, the proposed method has the following new features: (1) entropy information is adopted to derive personal weights for different users or services when computing PCC similarity; (2) location information and sample set similarity are considered to enhance PCC similarity; (3) topology information is introduced to address data sparsity issue; (4) neighbourhood information is incorporated with MF to improve prediction accuracy. We conduct an experiment on a real-world dataset which includes web service invocations from 339 service users on 5825 services to verify the feasibility and efficiency of our method. Keywords Cloud manufacturing · Quality of service · Collaborative filtering · Service recommendation · Matrix factorization
Introduction During the past two decades, rapid development and widespread application of information technology have greatly transformed conventional manufacturing. It becomes increasingly important to meet the dynamic requirements of a global marketplace for manufacturing enterprises (Wu et al. 2012). Faster time-to-market, higher quality, lower cost, better service, cleaner environment, greater flexibility and richer knowledge have become the new aim of manufacturing enterprises (Tao et al. 2011, 2017b). Collaboration, Internet of things and cloud computing have been identified as key technologies to reshape manufacturing industry (Xu 2012; Zhang et al. 2017). Under this background, cloud manufacturing (CMfg) is proposed as a new manufacturing paradigm, which could provide cost-effective, flexible, and
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Biqing Huang [email protected] Yu Feng [email protected]
1
Department of Automation, Tsinghua University, Beijing 100084, China
scalable solutions with lower support and maintenance costs (Tao et al. 2014b, 2017a). The concept of CMfg was first presented by Li et al. (2010). The characteristics, core technologies and typical applications of CMfg were extensively discussed (Li et al. 2012; Yin et al. 2011; Wu et al. 2013a). CMfg aims to break the barrier of structural differences and spatial distances in manufacturing collaboration (Jin e
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