Advertisement

Multimedia Tools and Applications

, Volume 76, Issue 18, pp 18193–18220 | Cite as

Discovering internal social relationship for influence-aware service recommendation

  • Wuhui Chen
  • Incheon Paik
  • Neil Y. Yen
Article
  • 279 Downloads

Abstract

Existing approaches, such as semantic content-based or Collaborative Filtering-based recommendations, fail to exploit social aspects of services because services lack social relationships and do not consider social influence. In this paper, we propose a methodology for connecting distributed services in a global social service network (GSSN) to facilitate discovering internal social relationship for social influence-aware service recommendation. First, we propose a novel platform for constructing a GSSN by linking distributed services with social links based on quality of social link. We then propose a flexible model of the effective awareness of social influence, which provides a quantitative measure of the strength of influence between services. Next, a novel social influence-aware service recommendation approach is proposed based on GSSN using internal social relationship among services. The experimental results demonstrated that our new approach can solve the service recommendation problem with a low usage threshold and high accuracy, where the user preferences are exploited by a recommend-as-you-go method.

Keywords

Social link Global social service network Recommend-as-you-go Social influence Service recommendation 

References

  1. 1.
    Albert R, Barabási A (2000) Topology of evolving networks: local events and universality. Phys Rev Lett 85:5234–5237CrossRefGoogle Scholar
  2. 2.
    Barabási A, Albert R (1999) Emergence of scaling in random networks. Science 286:509–512MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Bianconi G (2001) Competition and multiscaling in evolving networks. Europhys Lett 54:436–442CrossRefGoogle Scholar
  4. 4.
    Bianconi G, Barabási A-L (2001) Bose–Einstein condensation in complex networks. Phys Rev Lett 86(24):5632–5635CrossRefGoogle Scholar
  5. 5.
    Bizer C, Heath T, Lee TB (2009) Linked data—the story so far. J Semant Web Inf 5(3):1–22CrossRefGoogle Scholar
  6. 6.
    Chen X, Liu X, Huang Z, Sun H (2010) RegionKNN: a scalable hybrid collaborative filtering algorithm for personalized Web service recommendation. In Proc. 8th Int’l Conf. Web Services (ICWS’10), pp. 9–16Google Scholar
  7. 7.
    Chen W, Paik I, Hung PCK (2015) Constructing a global social service network for better quality of web service discovery. IEEE Trans Serv Comput 8(2):284–298CrossRefGoogle Scholar
  8. 8.
    Christakis NA, Fowler JH (2007) The spread of obesity in a large social network over 32 years. N Engl J Med 375(4):370–379CrossRefGoogle Scholar
  9. 9.
    Dong Y, Tang J, Wu S, Tian J, Chawla NV, Rao J, Cao H (2012) Link prediction and recommendation across heterogeneous social networks. In Proc. ICDM, pp.181–190Google Scholar
  10. 10.
    Jiang Y, Liu J, Tang M, Liu X (2011) An effective Web service recommendation based on personalized collaborative filtering. In Proc. 11th Int’l Conf. Web Services (ICWS’11), pp. 211–218Google Scholar
  11. 11.
    Klusch M, Fries B, Sycara K (2006) Automated semantic web service discovery with OWLS-MX. Proc. 15th IEEE Int’l Autonomous agents and multiagent systems Conf, pp.915–922Google Scholar
  12. 12.
    Kschischang FR, Frey BJ, Loeliger HA (2001) Factor graphs and the sum-product algorithm. IEEE Trans Inf Theory, pp. 498–519Google Scholar
  13. 13.
    Lecue F (2010) Combining collaborative filtering and semantic content-based approaches to recommend web services. In Proc. Int’l Conf. Semantic Computing, pp. 200–205Google Scholar
  14. 14.
    Lecue F, Mehandjiev N (2011) Seeking quality of Web service composition in a semantic dimension. IEEE Trans Knowl Data Eng 23(6):942–959CrossRefGoogle Scholar
  15. 15.
    Lee YJ, Kim CS (2009) A learning ontology method for RESTful semantic Web services. In Proc. 7th Int’l Conf. Web Services (ICWS’09)Google Scholar
  16. 16.
    Maamar Z, Bispo dos Santos P, Krug Wives L, Badr Y, Faci N, Palazzo Moreira de Oliveira J (2011) Using social networks for Web services discovery. IEEE Internet Comput 15(4):48–54CrossRefGoogle Scholar
  17. 17.
    Maamar Z, Faci N, Badr Y, Krug Wives L, Bispo dos Santos P, Benslimane D, Palazzo Moreira de Oliveira J (2011) Towards a framework for weaving social networks principles into web services discovery. 11th Annual Intl Conf. on New Technologies of Distributed Systems (NOTERE), pp. 9–13Google Scholar
  18. 18.
    Maamar Z, Hacid H, Huhns MN (2011) Why Web services need social networks. IEEE Internet Comput 15(2):90–94CrossRefGoogle Scholar
  19. 19.
    Paolucci M, Kawamura T, PayneTR, Sycara K (2002) Semantic matching of Web services capabilities. In Proc. of the 1st Int’l Semantic Web Conf, pp. 333–347Google Scholar
  20. 20.
    Pedrinaci C, Domingue J (2010) Toward the next wave of services: linked services for the Web of data. J Univ Comput Sci 16(13):1694–1719Google Scholar
  21. 21.
    Shao L, Zhang J, Wei Y, Zhao J, Xie B, Mei H (2007) Personalized QoS prediction for web services via collaborative filtering. In Proc. 5th Int’l Conf. Web Services (ICWS’07), pp. 439–446Google Scholar
  22. 22.
    Tan W, Zhang J, Madduri R, Foster I, De Roure D, Goble C (2011) Providing map and GPS assistance to service composition in bioinformatics. IEEE Intl Conf Serv ComputGoogle Scholar
  23. 23.
    Tang J, Sun J, Wang C, Yang Z (2009) Social influence analysis in large-scale networks. KDD’09, pp. 807–816Google Scholar
  24. 24.
    Wang G, Xu D, Qi Y, Hou D (2008) A semantic match algorithm for Web services based on improved semantic distance. Proc. 4th Int’l Conf. Next Generation Web Service PracticesGoogle Scholar
  25. 25.
    Wang FY, Zeng D, Carley KM, Mao W (2007) Social computing: from social informatics to social intelligence. IEEE Intell Syst 22(2):79–83CrossRefGoogle Scholar
  26. 26.
    Wu J, Chen L, Feng Y, Zheng Z, Zhou M, Wu Z (2013) Predicting quality of service for selection by neighborhood-based collaborative filtering. IEEE Trans Syst Man Cybern Syst 43(2):428–439CrossRefGoogle Scholar
  27. 27.
    Xia H, Yoshida T (2007) Web service recommendation with ontology-based similarity measure. In Proc. Int’l Conf. Innovative Computing, Information and Control. pp. 412–415, doi:  10.1109/ICICIC.2007.620
  28. 28.
    Xiong H, Vaidya J, Shafiq B, Paliwal AV, Adam N (2012) Semantics-based automated service discovery. IEEE Trans Serv Comput 5(2):260–275CrossRefGoogle Scholar
  29. 29.
    Zhang J, Tan W, Alexander J, Foster I, Madduri R (2011) Recommend-as-you-go: a novel approach supporting services-oriented scientific workflow reuse. IEEE Intl Conf Serv ComputGoogle Scholar
  30. 30.
    Zheng Z, Ma H, Lyu MR, King I (2013) Collaborative Web service QoS prediction via neighborhood integrated matrix factorization. IEEE Trans Serv Comput 6(3):289–299CrossRefGoogle Scholar
  31. 31.
    Zheng Z, Ma H, Lyu MR, King I (2011) QoS-aware Web service recommendation by collaborative filtering. IEEE Trans Serv Comput 4(2):140–152CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringUniversity of AizuFukushimaJapan

Personalised recommendations