Cloud service recommendation for small and medium-sized enterprises: A context-aware group decision making approach

Author:

Ma Hua1,Huang Zhuoxuan1,Zhang Xin2,Zhang Hongyu3,Wang Jianqiang3

Affiliation:

1. College of Information Science and Engineering, Hunan Normal University, Changsha, Hunan, China

2. College of Business, Austin Peay State University, Clarksville, TN, USA

3. School of Business, Central South University, Changsha, Hunan, China

Abstract

Recently, the enormous advantages of cloud services make them increasingly appealing to the small and medium-sized enterprises. The growing number of available services makes it challenging to select trustworthy services. Existing approaches focus on user preferences to guide personalized services recommendation for individual users, but lack of the research on trustworthy service recommendation for the small and medium-sized enterprises that represents a group user consisting of multiple individual users. For this type of enterprise, the cloud services recommendation must address the challenges from the diverse client context of individual users, the imprecise quality of experience in an uncertain cloud environment and the invalid or unsatisfactory recommendations. A client context-aware approach is proposed to recommend trustworthy cloud services for the small and medium-sized enterprises based on non-compensatory multi-criteria decision-making. In it, a type of client context is viewed as an independent evaluation criterion, and the interval neutrosophic numbers are employed to measure the fuzzy trustworthiness of cloud services. Based on the investigated outranking relations of interval neutrosophic numbers, a non-compensatory multi-criteria decision-making procedure via an improved ELECTRE III method is developed to rank candidate services. Experimental results demonstrate that this approach could efficiently produces the accurate ranking results of cloud services and effectively recommend the trustworthy service for small and medium-sized enterprises.

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

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