CC-PSM: A Preference-Aware Selection Model for Cloud Service Based on Consumer Community

7Citations
Citations of this article
7Readers
Mendeley users who have this article in their library.

Abstract

In order to give full consideration to the consumer's personal preference in cloud service selection strategies and improve the credibility of service prediction, a preference-Aware cloud service selection model based on consumer community (CC-PSM) is presented in this work. The objective of CC-PSM is to select a service meeting a target consumer's demands and preference. Firstly, the correlation between cloud consumers from a bipartite network for service selection is mined to compute the preference similarity between them. Secondly, an improved hierarchical clustering algorithm is designed to discover the consumer community with similar preferences so as to form the trusted groups for service recommendation. In the clustering process, a quantization function called community degree is given to evaluate the quality of community structure. Thirdly, a prediction model based on consumer community is built to predict a consumer's evaluation on an unknown service. The experimental results show that CC-PSM can effectively partition the consumers based on their preferences and has good effectiveness in service selection applications.

Cite

CITATION STYLE

APA

Wang, Y., Zhou, J. T., & Tan, H. Y. (2015). CC-PSM: A Preference-Aware Selection Model for Cloud Service Based on Consumer Community. Mathematical Problems in Engineering, 2015. https://doi.org/10.1155/2015/170656

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free