An Encoder-Decoder Architecture for the Prediction of Web Service QoS

  • Mohammed Ismail SmahiEmail author
  • Fethellah HadjilaEmail author
  • Chouki TibermacineEmail author
  • Mohammed MerzougEmail author
  • Abdelkrim BenamarEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11116)


Quality of Service (QoS) prediction is an important task in Web service selection and recommendation. Existing approaches to QoS prediction are based on either Content Filtering or Collaborative Filtering. In the two cases, these approaches use external data or past interactions between users and services to predict missing or future QoS scores. One of the most effective techniques for QoS prediction is Matrix Factorization (MF), with Latent Factor Models. The key idea of MF consists in learning a compact model for both users and services. Thereafter QoS prediction is simply computed as a dot product between the user’s latent model and the service’s latent model. Despite the successful results of MF in the recommendation area, there are still a set of problems that should be handled, like: (i) the sparsity of the input models, and (ii) the learning of the latent factors which is prone to over-fitting. In this paper, we propose an approach to solve these two problems by using a simple neural network, an auto-encoder, and by exploiting cross-validation on a well-known dataset, in order to select the ideal number of latent factors, and thereby reduce the over-fitting phenomenon.


  1. 1.
    Chen, S., Fan, Y., Tan, W., Zhang, J., Bai, B., Gao, Z.: Service recommendation based on separated time-aware collaborative poisson factorization. J. Web Eng. 16(7–8), 595–618 (2017)Google Scholar
  2. 2.
    Yueshen, X., Yin, J., Li, Y.: A collaborative framework of web service recommendation with clustering-extended matrix factorisation. Int. J. Web Grid Serv. 12(1), 1–25 (2016)CrossRefGoogle Scholar
  3. 3.
    Zhang, P., Han, Q., Li, W., Leung, H., Song, W.: A novel QOS prediction approach for cloud service based on Bayesian networks model. In: 2016 IEEE International Conference on Mobile Services (MS), pp. 111–118 (2016)Google Scholar
  4. 4.
    Rong, W., Peng, B., Ouyang, Y., Liu, K., Xiong, Z.: Collaborative personal profiling for web service ranking and recommendation. Inf. Syst. Front. 17(6), 1265–1282 (2015)CrossRefGoogle Scholar
  5. 5.
    Yin, J., Yueshen, X.: Personalised QOS-based web service recommendation with service neighbourhood-enhanced matrix factorisation. Int. J. Web Grid Serv. 11(1), 39–56 (2015)CrossRefGoogle Scholar
  6. 6.
    Lo, W., Yin, J., Li, Y., Zhaohui, W.: Efficient web service QOS prediction using local neighborhood matrix factorization. Eng. Appl. Artif. Intell. 38, 14–23 (2015)CrossRefGoogle Scholar
  7. 7.
    Yueshen, X., Yin, J., Deng, S., Xiong, N.N., Huang, J.: Context-aware QOS prediction for web service recommendation and selection. Expert Syst. Appl. 53, 75–86 (2016)CrossRefGoogle Scholar
  8. 8.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)CrossRefGoogle Scholar
  9. 9.
    Wang, X., Zhu, J., Zheng, Z., Song, W., Shen, Y., Lyu, M.R.: A spatial-temporal QOS prediction approach for time-aware web service recommendation. ACM Trans. Web 10(1), 7:1–7:25 (2016)CrossRefGoogle Scholar
  10. 10.
    Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. In: Rumelhart, D.E., McClelland, J.L., Corporate PDP Research Group (eds.): Parallel distributed processing: explorations in the microstructure of cognition, vol. 1, pp. 318–362 (1986)Google Scholar
  11. 11.
    Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Zhang, Y., Zheng, Z., Lyu, M.R.: WSPred: a time-aware personalized QOS prediction framework for web services. In: Proceedings of the IEEE 22nd International Symposium on Software Reliability Engineering (ISSRE 2011), pp. 210–219 (2011)Google Scholar
  13. 13.
    Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.-A.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371–3408 (2010)MathSciNetzbMATHGoogle Scholar
  14. 14.
    Golub, G.H., Reinsch, C.: Singular value decomposition and least squares solutions. In: Bauer, F.L., Householder, A.S., Olver, F.W.J., Rutishauser, H., Samelson, K., Stiefel, E. (eds.) Handbook for Automatic Computation, pp. 134–151. Springer, Heidelberg (1971) Scholar
  15. 15.
    Tang, M., Zheng, Z., Kang, G., Liu, J., Yang, Y., Zhang, T.: Collaborative web service quality prediction via exploiting matrix factorization and network map. IEEE Trans. Netw. Serv. Manage. 13(1), 126–137 (2016)CrossRefGoogle Scholar
  16. 16.
    Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 43–52 (1998)Google Scholar
  17. 17.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp. 285–295. ACM (2001)Google Scholar
  18. 18.
    Zheng, Z., Ma, H., Lyu, M.R., King, I.: WSRec: a collaborative filtering based web service recommendation system. In: Web Services, 2009, ICWS 2009. IEEE International Conference on Web Services (ICWS 2009), pp. 437–444. IEEE Computer Society (2009)Google Scholar
  19. 19.
    Zheng, Z., Ma, H., Lyu, M., King, I.: QOS-aware web service recommendation by collaborative filtering. IEEE Trans. Serv. Comput. 4(2), 140–152 (2011)CrossRefGoogle Scholar
  20. 20.
    Godse, M., Bellur, U., Sonar, R.: Automating QOS based service selection. In: IEEE International Conference on Web Services (ICWS), pp. 534–541. IEEE (2010)Google Scholar
  21. 21.
    Blumer, A., Ehrenfeucht, A., Haussler, D., Warmuth, M.K.: Occam’s razor. Inf. Process. Lett. 24(6), 377–380 (1987)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Shao, L., Zhang, J., Wei, Y., Zhao, J., Xie, B., Mei, H.: Personalized QOS prediction for web services via collaborative filtering. In: Proceedings of the IEEE International Conference on Web Services (ICWS 2007), pp. 439–446 (2007)Google Scholar
  23. 23.
    Ma, Y., Wang, S., Hung, P.C.K., Hsu, C.H., Sun, Q., Yang, F.: A highly accurate prediction algorithm for unknown web service QOS values. IEEE Trans. Serv. Comput. 9(4), 511–523 (2016)CrossRefGoogle Scholar
  24. 24.
    Kuang, L., Xia, Y., Mao, Y.: Personalized services recommendation based on context-aware QOS prediction. In: Proceedings of the IEEE 19th International Conference on Web Services (ICWS 2012), pp. 400–406. IEEE Computer Society (2012)Google Scholar
  25. 25.
    Deng, S., Huang, L., Guandong, X.: Social network-based service recommendation with trust enhancement. Expert Syst. Appl. 41(18), 8075–8084 (2014)CrossRefGoogle Scholar
  26. 26.
    Chen, Z., Shen, L., Li, F.: Exploiting web service geographical neighborhood for collaborative QOS prediction. Future Gener. Comput. Syst. 68, 248–259 (2017)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2018

Authors and Affiliations

  1. 1.LRITUniversity of TlemcenTlemcenAlgeria
  2. 2.LIRMM, CNRS and University of MontpellierMontpellierFrance

Personalised recommendations