Recommend trustworthy services using interval numbers of four parameters via cloud model for potential users

Abstract

How to discover the trustworthy services is a challenge for potential users because of the deficiency of usage experiences and the information overload of QoE (quality of experience) evaluations from consumers. Aiming to the limitations of traditional interval numbers in measuring the trustworthiness of service, this paper proposed a novel service recommendation approach using the interval numbers of four parameters (INF) for potential users. In this approach, a trustworthiness cloud model was established to identify the eigenvalue of INF via backward cloud generator, and a new formula of INF possibility degree based on geometrical analysis was presented to ensure the high calculation precision. In order to select the highly valuable QoE evaluations, the similarity of client-side feature between potential user and consumers was calculated, and the multi-attributes trustworthiness values were aggregated into INF by the fuzzy analytic hierarchy process method. On the basis of ranking INF, the sort values of trustworthiness of candidate services were obtained, and the trustworthy services were chosen to recommend to potential user. The experiments based on a realworld dataset showed that it can improve the recommendation accuracy of trustworthy services compared to other approaches, which contributes to solving cold start and information overload problem in service recommendation.

Keywords

service recommendation / trustworthiness / interval numbers of four parameters / cloud model / potential users

References

[1] Casas P, Schatz R. Quality of experience in cloud services: survey and measurements. Computer Networks, 2014, 68(1): 149−165

[2] Rosaci D, Sarne G M L. Recommending multimedia Web services in a multi-device environment. Information Systems, 2013, 38(2): 198−212

[3] Chen X, Zheng Z B, Yu Q, Lyu M R. Web service recommendation via exploiting location and QoS information. IEEE Transactions on Parallel and Distributed Systems, 2014, 25(7): 1913−1924

[4] Ma H, Hu Z G, Zhang H Y. Personalized fusion method of service trust evaluation in cloud computing environment. Journal of Chinese Computer Systems, 2014, 35(4): 776−780

[5] Lin Y M, Wang X L, Zhou T Z. Survey on quality evaluation and control of online reviews. Journal of Software, 2014, (253): 506−527, 2014

[6] Lüa L Y, Medob M, Yeung C H. Recommender systems. Physics Reports. 2012, 519(2): 1−49

[7] Pereira R, Lopes H, Breitman K, Mundim V, Peixoto W. Cloud based real-time collaborative filtering for item-item recommendations. Computers in Industry, 2014, 65(2): 279−290

[8] Zhang Y L, Zheng Z B, Lyu M R. Exploring Latent Features for Memory-Based QoS Prediction in Cloud Computing. In: Proceedings of the 30th IEEE Symposium on Reliable Distributed Systems. 2011: 1−10

[9] Chen X, Liu X D, Huang Z C, Sun H L. RegionKNN: a scalable hybrid collaborative filtering algorithm for personalized web service recommendation. In: Proceedings of the IEEE International Conference on Web Services. 2010, 9−16

[10] Lo W, Yin J W, Deng S G, Li Y, Wu Z H. Collaborative web service QoS prediction with location-based regularization, In: Proceedings of the IEEE International Conference on Web Services. 2012, 464−471

[11] Tang M D, Jiang Y C, Liu J X. User location-aware Web services QoS prediction. Journal of Chinese Computer Systems, 2012, 33(12): 2664−2668

[12] Ma W L, Zhu L N, Wang W L. Cloud service selection model based on QoS-aware in cloud manufacturing environment. Computer Integrated Manafacturing Systems, 2014, 20(5): 1246−1254

[13] Mohamad M, Nizar B, Jamal B. Probabilistic approach for QoS-aware recommender system for trustworthy web service selection. Applied Intelligence, 2014, 41(2): 503−524

[14] Wen T, Li Y Q, Sheng G J. Improved PSO-based web service selection under uncertain information. Journal of Jilin University (Engineering and Technology Edition), 2014, 44(1): 129−136

[15] Zhang L C, Qing C. Hybrid-context-aware web service selection approach. Journal of Internet Technology, 2013, 14(1): 57−69

[16] Hu Q Z, Zhang W H, Research and Its Application of Interval Numbers Theory. Beijing: Science Press, 2010

[17] Hu Q Z, Yu L, Zhang A P. Research about multi-objective decision making method based on interval numbers of three elements. Journal of Systems and Management, 2010, 19(1): 25−30

[18] Hu J H, Lin Z Y. Multi-criteria decision making method based on interval numbers of four parameters. Operations Research and Management Science, 2013, 22(6): 84−91

[19] Li D Y, Meng H J, Shi X M. Membership clouds and membership cloud generators. Journal of Computer Research and Development, 1995, 32(6): 15−20

[20] Liu Y C, Zhang H S, Ma Y T, Li D Y, Chen G S. Collective intelligence and uncertain knowledge representation in cloud computing. China Communications, 2011, 27(3): 58−66

[21] Wang G Y, Xu C L, Li D Y. Generic normal cloud model. Information Sciences, 2014, 280: 1−15

[22] Liu C Y, Feng M, Dai X J, Li D Y. A new algorithm of backward cloud. Journal of System Simulation, 2004, 16(11): 2417−2420

[23] Xu Z H, Da Q L. Possibility degree method for ranking interval numbers and its applicaion. Journal of System Engineering, 2003, 18(1): 67−70

[24] Mikhailov L, Tsvetinov P. Evaluation of services using a fuzzy analytic hierarchy process. Applied Soft Computing, 2004, 5(1): 23−33

[25] Xu Z S. Two methods of maximizing deviations of multi-attribute decision making. Journal of Industrial Engineering and Engineering Management, 2001, 15(2): 21−29

[26] Zheng Z B, Ma H, Lyu M R, King I. Collaborative Web service QoS prediction via neighborhood integrated matrix factorization. IEEE Transactions on Services Computing, 2013, 6(3): 289−299

[27] Ding S, Yang S L, Zhang Y T, Liang C Y, Xia C Y. Combining QoS prediction and customer satisfaction estimation to solve cloud service trustworthiness evaluation problems. Knowledge-Based Systems, 2014, 56: 216−225

[28] Ding S, Xia C Y, Zhou K L, Yang S L, Shang J S. Decision support for personalized cloud service selection through multi-Attribute trustworthiness evaluation. PLOS ONE, 2014, 9(6): e97762

[29] Al-Masri E, Mahmoud Q H. Web service discovery and client goals. Computer, 2009, 42(1): 104−107

[30] Ma H, Hu Z G, Zhang H Y. Personalized recommendation framework for trustworthy services in cloud paradigm. Journal of Chinese Computer Systems, 2014, 35(5): 967−972

[31] Armbrust M, Fox A, Griffith R, Joseph A D, Katz R, Konwinski A, Lee G, Patterson D, Rabkin A, Stoica I, Zaharia M. A view of cloud computing. Communications of the ACM, 2010, 53(4): 50−58

[32] Wang P, Zhang S Y, Chen X J. A novel reputation reporting mechanism based on cloud model and gray system theory. International Journal of Advancements in Computing Technology, 2011, 3(10): 75−84

[33] Hu C H, Chen X H, Wu M, Liu J X. A service trust negotiation and access control strategy based on SLA in cloud computing. Science in China Series E—Information Sciences, 2012, 42(3): 314−332

[34] Shen HY, Liu GX. An efficient and trustworthy resource sharing platform for collaborative cloud computing. IEEE Transactions on Parallel and Distributed Systems, 2014, 25(4): 862−875

[35] Huang X D. UsageQoS: estimating the QoS of Web services through online user communities. ACM Transactions on the Web, 2013, 8(1): 1−31

[36] Mo Y J, Chen J W, Xie X, Luo C Q, Yang L T. Cloud-based mobile multimedia recommendation system with user behavior information. IEEE Systems Journal, 2014, 8(1): 184−193

[37] Xia Y N, Zhou M C, Luo X, Zhu Q S, Li J, Huang Y. Stochastic modeling and quality evaluation of infrastructure-as-a-service clouds. IEEE Transactions on Automation Science and Engineering, 2015, 12(1): 162−170

[38] Li W F, Zhong Y, Wang X, Cao Y L. Resource virtualization and service selection in cloud logistics. Journal of Network and Computer Applications, 2013, 36(6): 1696−1704

[39] Luo X, You Z H, Zhou M C, Li S, Leung H, Xia Y N, Zhu Q S. A highly efficient approach to protein interactome mapping based on collaborative filtering framework. Scientific Reports, 2015, 5: 7702

[40] Li Y, Cao B, Xu L D, Yin J W, Deng S G, Yin Y Y, Wu Z H. An efficient recommendation method for improving business process modeling. IEEE Transactions on Industrial Informatics, 2014, 10(1): 502−513

[41] Luo X, Xia Y, Zhu Q. Incremental collaborative filtering recommender based on regularized matrix factorization. Knowledge-Based Systems, 2012, 27: 271−280

[42] Luo X, Xia Y N, Zhu Q S, Li Y. Boosting the k-nearest-neighborhood based incremental collaborative filtering. Knowledge-Based Systems, 2013, 53: 90−99

[43] Xu H L, Wu X, Li X D, Yan B P. Comparison study of Internet recommendation system. Journal of Software, 2009, 20(2): 350−362.

[44] Zheng Z B, Lyu M R. Collaborative reliability prediction for serviceoriented systems. In: Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering. 2010, 35−44

[45] Sun X H, Kong F S, Ye S. A comparison of several algorihtms of collaborative filtering in startup stage. In: Proceedings of the IEEE Intenrational Conefrenee on Networking, Sensing and Controlling. 2005, 25−28

[46] Yu K, Schwaighofer A, Tresp V, Xu X W, Kriegel H P. Probabilistic memory-based collaborative filtering. IEEE Transactions on Knowledge and Data Engineering, 2004, 16(1): 56−69

[47] Rashid A L, Albert I, Cosley D, Lam S K, Mcnee S M, Konstan J A, Riedl J. Getting to know you: learning new user preferences in recommender systems. In: Proceedings of the Intenrational Conefrenee on Intelligent User Interfaces. 2002, 127−134

[48] Jannach D, Zanker M, Felfernig A, Feiedrich G. Recommender systems: an intruoduction. Cambridge: Cambridge University Press, 2011

[49] Pan S J, Yang Q. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10): 1345−1359

[50] Nori N, Bollegala D, Kashima H. A dimension reduction approach to multinomial relation prediction. Transactions of the Japanese Society for Artificial Intelligence, 2014, 29(1): 168−176

[51] Liu B, Xiong H, Papadimitriou S, Fu Y J, Yao Z J. A general geographical probabilistic factor model for point of interest recommendation. IEEE Transactions on Knowledge and Data Engineering, 2015, 27(5): 1167−1179

[52] Gao F J. Possibility degree and comprehensive priority of interval numbers. Systems Engineering—Theory and Practice, 2013, 33(8): 2033−2040

[53] Zhang H Y, Wang J Q, Chen X H. An outranking approach for multicriteria decision-making problems with interval-valued neutrosophic sets. Neural Computing and Applications, 2015

[54] Sun L, Dong H, Hussain F K, Hussain O K, Chang E. Cloud service selection: state-of-the-art and future research directions. Journal of Network and Computer Applications, 2014, 45: 134−150

[55] Weinhardt C, Anandasivam A, Blau B, Stosser J. Business models in the service world. IT Professional, 2009, 11(2): 28−33

[56] Fardin A M, Naser N B, Ali N M. Empower service directories with knowledge. Knowledge-Based Systems, 2012, 30: 172−184

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg