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Utilizing customer satisfaction in ranking prediction for personalized cloud service selection
Stockholm University, Faculty of Social Sciences, Stockholm Business School. Chinese Academy of Sciences, China.
Number of Authors: 4
2017 (English)In: Decision Support Systems, ISSN 0167-9236, E-ISSN 1873-5797, Vol. 93, 1-10 p.Article in journal (Refereed) Published
Abstract [en]

With the rapid development of cloud computing, cloud service has become an indispensable component of modern information systems where quality of service (QoS) has a direct impact on the system's performance and stability. While scholars have concentrated their efforts on the monitoring and evaluation of QoS in cloud computing, other service selection characteristics have been neglected, such as the scarcity of evaluation data and various customer needs. In this paper, we present a ranking-oriented prediction method that will assist in the process of discovering the cloud service candidates that have the highest customer satisfaction. This approach encompasses two basic functions: ranking similarity estimation and cloud service ranldng prediction that takes into account customer's preference and expectation. The comparative experimental results show that the proposed method outperforms other competing methods.

Place, publisher, year, edition, pages
2017. Vol. 93, 1-10 p.
Keyword [en]
Cloud service, QoS ranking, Collaborative filtering, Similarity computation, Customer satisfaction
National Category
Computer and Information Science Economics and Business
Identifiers
URN: urn:nbn:se:su:diva-140391DOI: 10.1016/j.dss.2016.09.001ISI: 000392676100001OAI: oai:DiVA.org:su-140391DiVA: diva2:1083040
Available from: 2017-03-20 Created: 2017-03-20 Last updated: 2017-03-20Bibliographically approved

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