Bayesian Personalized Ranking for Optimized Personalized QoS Ranking

Author

Patil, Pranjali M. ; Wagh, R.B.

Author_Institution

Dept. of Comput. Eng., R.C. Patel I.T. Shirpur, Dhule, India

fYear

2015

fDate

26-27 Feb. 2015

Firstpage

310

Lastpage

314

Abstract

Cloud computing is computing allow centralized data storage and online access to resources. The main research problem is to build highly qualitative cloud application. To select optimal cloud services quality of service is going to provide favorable or optimal information. Quality of service ranking is very time consuming and costly as it requires real world invocation. So to avoid this real world invocation, past usage experience is used. In this paper there are two datasets which contain user-item matrix of 300×500 and 339×5825 each for response time and throughput. Bayesian Personalized Ranking approaches give optimized personalized ranking to attain better accuracy.

Keywords

Bayes methods; cloud computing; data analysis; matrix algebra; quality of service; Bayesian personalized ranking approaches; centralized data storage; cloud computing; datasets; optimal cloud services quality of service; personalized QoS ranking optimization; qualitative cloud application; real world invocation; response time; user-item matrix; Bayes methods; Cloud computing; Collaboration; Quality of service; Throughput; Time factors; Bayesian personalized ranking; Cloud services; quality of service; user-item matrix;

fLanguage

English

Publisher

ieee

Conference_Titel

Computing Communication Control and Automation (ICCUBEA), 2015 International Conference on

Conference_Location

Pune

Type

conf

DOI

10.1109/ICCUBEA.2015.65

Filename

7155857