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A Novel Service Recommendation Approach Considering the User’s Trust Network

  • Guoqiang Li
  • Zibin Zheng
  • Haifeng WangEmail author
  • Zifen Yang
  • Zuoping Xu
  • Li Liu
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 201)

Abstract

Web services are ever increasingly published on the network as core components of Service-oriented architecture (SOA). An attendant problem is how to help users select their satisfied services that meet their functional and non-functional requirements from the mass services. Service recommendation technology is adopted and studied as an effective approach currently. This paper focuses on the user’s trust network, where the users share their experience and rating for the invoked services. To attack the data sparsity and cold-start problems in the user-service rating matrix, an improved random walk algorithm is proposed. Firstly, we employ the non-negative matrix factorization method to compute the similarities between users and services separately. Then our method introduces the trust relationship in iterations of the random walk to select the trust users accurately. At last, the real dataset is used to validate our approach. Experimental results show the effectiveness of our approach compared with the state-of-art algorithms.

Keywords

Web service Service recommendation Social network Random walk 

Notes

Acknowledgments

This work was funded by the Natural Science Foundation of Shandong Province (NSFS Grant No. ZR2014FL013) and the Independent Innovation and Achievements Transformation Special Project of Shandong Province (No. 2014ZZCX02702). The authors acknowledge the support of the Opening Fund of Shandong Provincial Key Laboratory for Network Based Intelligent Computing.

References

  1. 1.
    Al-Masri, E., Mahmoud, Q.H.: QoS-based discovery and ranking of web services. In: The 16th International Conference on Computer Communications and Networks, pp. 529–534. IEEE Press, Honolulu, Hawaii (2007)Google Scholar
  2. 2.
    Zheng, Z., Ma, H., Lyu, M.R., King, I.: WSRec: a collaborative filtering based web service recommender system. In: The 16th International Conference on Web Services, pp. 437–444. IEEE Computer Society, Los Angeles (2009)Google Scholar
  3. 3.
    Massa, P., Avesani, P.: Trust-aware collaborative filtering for recommender systems. In: Meersman, R., Tari, Z. (eds.) OTM 2004. LNCS, vol. 3290, pp. 492–508. Springer, Heidelberg (2004). doi: 10.1007/978-3-540-30468-5_31 CrossRefGoogle Scholar
  4. 4.
    Wang, S., Hsu, C.-H., Liang, Z., Sun, Q.: Multi-user web service selection based on multi-QoS prediction. Inf. Syst. Front. 16(1), 143–152 (2014)CrossRefGoogle Scholar
  5. 5.
    Chen, X., Zheng, Z., Yu, Q., Lyu, M.R.: Web service recommendation via exploiting location and QoS information. IEEE Trans. Parallel Distrib. Syst. 25(7), 1913–1924 (2014)CrossRefGoogle Scholar
  6. 6.
    He, P., Zhu, J., Zheng, Z., Xu, J., Lyu, M.R.: Location-based hierarchical matrix factorization for web service recommendation. In: The 21st International Conference on Web Services, pp. 297–304. IEEE Computer Society, Alaska (2014)Google Scholar
  7. 7.
    Zheng, Z., Ma, H., Lyu, M.R., King, I.: Collaborative web service QoS prediction via neighborhood integrated matrix factorization. IEEE Trans. Serv. Comput. 6(3), 289–299 (2013)CrossRefGoogle Scholar
  8. 8.
    Yu, Q., Zheng, Z., Wang, H.: Trace norm regularized matrix factorization for service recommendation. In: 20th IEEE International Conference on Web Services, pp. 34–41. IEEE Computer Society, Santa Clara (2013)Google Scholar
  9. 9.
    Li, Z., Cao, J., Gu, Q.: Temporal-aware QoS-based service recommendation using tensor decomposition. J. Web Serv. Res. 12(1), 62–74 (2015)CrossRefGoogle Scholar
  10. 10.
    Zhang, R., Li, C., Sun, H., Wang, Y., Huai, J.: Quality of web service prediction by collective matrix factorization. In: 11th International Conference on Service Computing, pp. 432–439. IEEE Xplore Press, Bangalore (2014)Google Scholar
  11. 11.
    Abdullah, A.: An integrated-model QoS-based graph for web service recommendation. In: 22nd International Conference on Web Services, pp. 416–423. IEEE Computer Society, New York (2015)Google Scholar
  12. 12.
    Golbeck, J.A.: Computing and applying trust in web-based socail networks, University of Maryland (2005)Google Scholar
  13. 13.
    Dongyan, J., Fuzhi, Z.: A collaborative filtering recommendation algorithm based on double neighbor choosing strategy. J. Comput. Res. Dev. 50(5), 1076–1084 (2013)Google Scholar
  14. 14.
    He, J., Chu, W.W.: Social networ-based recommender system (SNRS). In: Memon, N., Xu, J.J., Hicks, D.L., Chen, H. (eds.) Data Mining for Social Network Data. Annals of Information Systems, vol. 12, pp. 47–74. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  15. 15.
    Ray, S., Mahanti, A.: Improving prediction accuracy in trust-aware recommender systems. In: 43rd International Conference on System Sciences, pp. 1–9. IEEE Computer Society, New York (2010)Google Scholar
  16. 16.
    Tang, M., Xu, Y., Liu, J., Zheng, Z., Liu, X.F.: Trust-aware service recommendation via exploiting social networks. In: 10th IEEE International Conference on Services Computing, pp. 376–383. IEEE Computer Society, Santa Clara (2013)Google Scholar
  17. 17.
    Jamali, M., Ester, M.: TrustWalker: a random walk model for combining trust-based and item-based recommendation. In: 15th International Conference on Knowledge Discovery and Data Mining, pp. 397–406. ACM, Paris, France (2009)Google Scholar
  18. 18.
    Deng, S., Huang, L., Xu, G.: Social network-based service recommendation with trust enhancement. Expert Syst. Appl. 41(18), 8075–8084 (2014)CrossRefGoogle Scholar
  19. 19.
    Tang, M., Dai, X., Cao, B., Liu, J.: WSWalker: a random walk method for Qos-aware web service recommendation. In: 22th International Conference on Web Services, pp. 591–598. IEEE Computer Society, New York (2015)Google Scholar
  20. 20.
    Hoyer, P.O.: Non-negative matrix factorization with sparseness constraints. J. Mach. Learn. Res. 5, 1457–1469 (2004)MathSciNetzbMATHGoogle Scholar
  21. 21.
    Victor, P., Cornelis, C., De Cock, M., Teredesai, A.: Trust-and distrust-based recommendations for controversial reviews. IEEE Intell. Syst. 26(1), 48–55 (2011)CrossRefGoogle Scholar
  22. 22.
    Massa, P., Avesani, P.: Trust-aware recommender systems. In: 1st Conference on Recommender Systems, pp. 17–24. ACM (2007)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017

Authors and Affiliations

  • Guoqiang Li
    • 1
  • Zibin Zheng
    • 2
  • Haifeng Wang
    • 1
    Email author
  • Zifen Yang
    • 1
  • Zuoping Xu
    • 1
  • Li Liu
    • 1
  1. 1.School of InformaticsLinyi UniversityLinyiChina
  2. 2.Mobile Internet and Financial Big Data LabSun Yat-Sen UniversityGuangzhouChina

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