Abstract
The number of Internet Web services has become increasingly large recently. Cloud services consumers face a critical challenge in selecting services from abundant candidates. Due to the uncertainty of Web service QoS and the diversity of user characteristics, this paper proposes a Web service recommendation method based on cloud model and user personality (WSRCP), which employs cloud model similarity method to analyze the similarity of QoS feedback data among different users, to identify the user with high similarity to the potential user. Based on the QoS data of the users’ feedback, Finally, user characteristic attribute Web service recommendation is implemented by personalized collaborative filtering algorithm. The experimental results on the WS-Dream dataset show that our approach not only solves the drawbacks of the sparse user service, but also improves the recommend accuracy.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Zheng, Z., Ma, H., Lyu, M.R., et al.: QoS-aware web service recommendation by collaborative filtering. IEEE Trans. Serv. Comput. 4(5), 140–152 (2011)
Wu, J., Chen, L., Feng, Y., et al.: Predicting quality of service for selection by neighborhood-based collaboration filtering. IEEE Trans. Syst. Man Cybern. 43(2), 428–439 (2013)
Kang, G., Liu, J., Tang, M., Cao, B., Xu, Y.: An effective web service ranking method via exploring user behavior. IEEE Trans. Netw. Serv. Manag. 12, 554–564 (2015). ISSN 1932-4537
Zhang, G., Li, D., Li, P., et al.: Collaborative filtering recommendation algorithm based on cloud model. J. Softw. 18(10), 2403–2411 (2007)
Chen, Z., Li, Z.: Collaborative filtering recommendation algorithm based on user characteristics and project attributes. J. Comput. Appl. 07, 1748–1750, 1755 (2011)
Tang, M., Jiang, Y., Liu, J.: User location aware web service QoS prediction method. Small Micro-comput. Sys. 12, 2664–2668 (2012)
Liu, F., Hong, Y.: Communication algorithm based on user characteristic attribute and cloud model. Comput. Eng. Sci. 6, 1172–1176 (2014)
Li, D.: Uncertainty in knowledge representation. Eng. Sci. 10, 73–79 (2000)
Zadeh, L.A.: Fuzzy Logic with Engineering Applications. Publishing House of Electronics Industry, Beijing (2001)
Li, D., Liu, C.: The universal of normal cloud model. Eng. Sci. 6(8), 28–34 (2004)
Li, D., Liu, C., Gan, W.: A new cognitive model: cloud model. Int. J. Intell. Sys. 3(24), 357–375 (2009)
Li, D., Liu, C.: On the universality of cloud model. China Eng. Sci. 08, 28–34 (2004)
Liao, L., Li, C., Meng, X.: Free-model co-filtering algorithm based on Euclidean space similarity. Comput. Eng. Sci. 10, 1977–1982 (2015)
Li, H., Guo, C., Qiu, W.: Equivalent cloud model similarity calculation method. Acta Electron. Sin. 11, 2561–2567 (2011)
Zheng, Z., Zhang, Y., Lyu, M.R.: Investigating QoS of real-world web services. IEEE Trans. Serv. Comput. 7(1), 32–39 (2014)
Zheng, Z., Zhang, Y., Lyu, MR.: Distributed QoS evaluation for real-world web services. In: Proceedings of the 8th International Conference on Web Services (ICWS 2010), Miami, Florida, USA, 5–10 July 2010, pp. 83–90 (2010)
Llinas, G.A.G., Nagi, R.: Network and QoS-based selection of complementary services. IEEE Trans. Serv. Comput. 9(1), 79–91 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Yao, J., Hu, Z., Ma, H., Jiang, B. (2017). A Novel Recommendation Service Method Based on Cloud Model and User Personality. In: Zou, B., Li, M., Wang, H., Song, X., Xie, W., Lu, Z. (eds) Data Science. ICPCSEE 2017. Communications in Computer and Information Science, vol 727. Springer, Singapore. https://doi.org/10.1007/978-981-10-6385-5_16
Download citation
DOI: https://doi.org/10.1007/978-981-10-6385-5_16
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-6384-8
Online ISBN: 978-981-10-6385-5
eBook Packages: Computer ScienceComputer Science (R0)