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.
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