Research ArticleOpen AccessGoogle Scholar indexed
A Personalized Cloud Services Recommendation Based on Cooperative Relationship between Services
Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts & Telecommuni- cations, Beijing, China
Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts & Telecommuni- cations, Beijing, China
Beijing University of Posts & Telecommunications, Beijing, China
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts & Telecommunications, Beijing, China
- 1 Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts & Telecommuni- cations, Beijing, China
- 2 Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts & Telecommuni- cations, Beijing, China
- 3 Beijing University of Posts & Telecommunications, Beijing, China
- 4 State Key Laboratory of Networking and Switching Technology, Beijing University of Posts & Telecommunications, Beijing, China
Journal of Software Engineering and Applications·Volume 06 (2013)·Pages 623–629·Published 5 December 2013·DOI10.4236/jsea.2013.612074
Copy link · social · email
Abstract
A personalized recommendation for cloud services, which is based on usage history and the cooperative relationship of cloud services, is presented. According to service groups, a service group could be defined as several services that were used together by one user at a time, and cooperative relationship between each two services can be calculated. In the process of recommendation, the services which are highly related to the service that the user has selected would be obtained firstly, the result should then take the QoS (Quality of Service) similarity between service’s QoS and user’s preference into account, so the final result combining the cooperative relationship and similarity will meet the functional needs of users and also meet the user ’ s personalized non-functional requirements. The simulation proves that the algorithm works effectively.
KeywordsPersonalized RecommendationCloud ServiceQuality of ServiceSimilarityCooperative Relationship
- I. Cantador, M. Fe rnáandez and P. Castells, “A Collaborative Recommendation Framework for Ontology Evaluation and Reuse,” Universidad Autóonoma de Madrid, Spain, 2006.
- G. Adomavicius and A. Tuzhilin, “Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions,” IEEE Educational Activities Department, Vol. 17, No. 6, 2005, pp. 734-749.
- J. B. Schafer, D. Frankowski, J. Herlocker and S. Sen, “Collaborative Filtering Recommender Systems,” Adaptive Web, 2007.
- Knowledge & Data Engineering Group (KDE), “Information Systems and Machine Learning Lab (ISMLL),” Tag Recommendations in Folksonomies, 2006.
- J.-G. Liu, T. Zhou, B.-H. Wang, Y.-C. Zhang and Q. Guo, “Degree Correlation of Bipartite Network on Personalized Recommendation,” International Journal of Modern Physics C, Vol. 21, No. 1, 2010, pp. 137-147. http://dx.doi.org/10.1142/S0129183110014999
- C.-X. Jia, R.-R. Liu, D. Sun and B.-H. Wang, “A New Weighting Method in Network-Based Recommendation,” Physics A, Vol. 387, 2008, pp. 5887-5891.
- W. F. Pan, B. Li, B. Shao and P. He, “Software-Based Network Services and the Recommended Method of Automatic Classification,” Computer Engineering (Chinese Journal of Computers), Vol. 34, No. 12, 2011, pp. 2355-2369.
- T. Zhou, J. Ren, M. Medo and Y.-C. Zhang, “Bipartite Network Projection and Personal Recommendation,” Physical Review E, Vol. 76, No. 4, 2007, Article ID: 046115.
- X. Pan, G. S. Deng and J.-G. Liu, “Weighted Bipartite Network and Personalized Recommendation,” Physics Procedia, Vol. 3, No. 5, 2010, pp. 1867-1876. http://dx.doi.org/10.1016/j.phpro.2010.07.031
- G. Versa, “Sharon A Three Time-Weighted Figure Network Labels Recommended Method,” Computer Science, Vol. 39, No. 8, 2012, pp. 96-98.
- S. Kang, S. Kang and S. Hur, “A Design of the Conceptual Architecture for a Multitenant SaaS Application Platform,” Computers, Networks, Systems and Industrial Engineering (CNSI), 2011 First ACIS/JNU International Conference on Digital 2011, pp. 462-467.
- K. S. Gopalan and S. Nathan, “A Cloud Based Service Architecture for Personalized Media Recommendations,” International Conference on Next Generation Mobile Applications, Services, and Technologies, 2011, pp. 19-24.
- P. Bedi, H. Kaur and B. Gupta, “Trustworthy, Service Provider Selection in Cloud Computing Environment,” International Conference on Communication Systems and Network Technologies, 2012, pp. 714-718.