WCP-Nets: A Weighted Extension to CP-Nets for Web Service Selection

  • Hongbing Wang
  • Jie Zhang
  • Wenlong Sun
  • Hongye Song
  • Guibing Guo
  • Xiang Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7636)


User preference often plays a key role in personalized applications such as web service selection. CP-nets is a compact and intuitive formalism for representing and reasoning with conditional preferences. However, the original CP-nets does not support fine-grained preferences, which results in the inability to compare certain preference combinations (service patterns). In this paper, we propose a weighted extension to CP-nets called WCP-nets by allowing users to specify the relative importance (weights) between attribute values and between attributes. Both linear and nonlinear methods are proposed to adjust the attribute weights when conflicts between users’ explicit preferences and their actual behaviors of service selection occur. Experimental results based on two real datasets show that our method can effectively enhance the expressiveness of user preference and select more accurate services than other counterparts.


User Preference Real Dataset Service Selection Nonlinear Method Attribute Weight 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Al-Masri, E., Mahmoud, Q.: Discovering the best web service. In: Proceedings of the 16th International Conference on World Wide Web, pp. 1257–1258 (2007)Google Scholar
  2. 2.
    Bacchus, F., Grove, A.: Graphical models for preference and utility. In: Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, pp. 3–10. Morgan Kaufmann Publishers Inc. (1995)Google Scholar
  3. 3.
    Boutilier, C., Bacchus, F., Brafman, R.: Ucp-networks: A directed graphical representation of conditional utilities. In: Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, pp. 56–64 (2001)Google Scholar
  4. 4.
    Boutilier, C., Brafman, R., Domshlak, C., Hoos, H., Poole, D.: Cp-nets: A tool for representing and reasoning with conditional ceteris paribus preference statements. J. Artif. Intell. Res. (JAIR) 21, 135–191 (2004)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Brafman, R., Domshlak, C.: Preference handling-an introductory tutorial. AI Magazine 30(1), 58 (2009)Google Scholar
  6. 6.
    Brafman, R., Domshlak, C., Shimony, S.: On graphical modeling of preference and importance. Journal of Artificial Intelligence Research 25(1), 389–424 (2006)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Châtel, P., Truck, I., Malenfant, J., et al.: Lcp-nets: A linguistic approach for non-functional preferences in a semantic soa environment. Journal of Universal Computer Science 16(1), 198–217 (2010)Google Scholar
  8. 8.
    García, J., Ruiz, D., Ruiz-Cortés, A., Parejo, J.: Qos-aware semantic service selection: An optimization problem. In: Proceedings of 2008 IEEE Congress on Services-Part I, pp. 384–388 (2008)Google Scholar
  9. 9.
    Gavanelli, M., Pini, M.: Fcp-nets: extending constrained cp-nets with objective functions. In: Constraint Solving and Constraint Logic Programming, ERCIM (2008)Google Scholar
  10. 10.
    Koriche, F., Zanuttini, B.: Learning conditional preference networks. Artificial Intelligence 174(11), 685–703 (2010)MathSciNetzbMATHCrossRefGoogle Scholar
  11. 11.
    Lamparter, S., Ankolekar, A., Studer, R., Grimm, S.: Preference-based selection of highly configurable web services. In: Proceedings of the 16th International Conference on World Wide Web, pp. 1013–1022. ACM (2007)Google Scholar
  12. 12.
    Liu, J., Chang, H., Hsu, T., Ruan, X.: Prediction of the flow stress of high-speed steel during hot deformation using a bp artificial neural network. Journal of Materials Processing Technology 103(2), 200–205 (2000)CrossRefGoogle Scholar
  13. 13.
    Luenberger, D., Ye, Y.: Linear and nonlinear programming, vol. 116. Springer (2008)Google Scholar
  14. 14.
    O’Sullivan, D., Smyth, B., Wilson, D., Mcdonald, K., Smeaton, A.: Improving the quality of the personalized electronic program guide. User Modeling and User-Adapted Interaction 14(1), 5–36 (2004)CrossRefGoogle Scholar
  15. 15.
    Santhanam, G.R., Basu, S., Honavar, V.G.: TCPCompose  ⋆  – A TCP-Net Based Algorithm for Efficient Composition of Web Services Using Qualitative Preferences. In: Bouguettaya, A., Krueger, I., Margaria, T. (eds.) ICSOC 2008. LNCS, vol. 5364, pp. 453–467. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  16. 16.
    Sun, X., Liu, J.: Representation and realization of binary-valued cp-nets in single-branch tree. In: Proceedings of the 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, FSKD, vol. 4, pp. 1908–1911 (2010)Google Scholar
  17. 17.
    Wang, H., Liu, W.: Web service selection with quantitative and qualitative user preferences. In: Proceedings of IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, vol. 1, pp. 404–411 (2011)Google Scholar
  18. 18.
    Wang, H., Zhang, J., Tang, Y., Shao, S.: Collaborative approaches to complementing qualitative preferences of agents for effective service selection. In: Proceedings of the 2011 IEEE International Conference on Tools with Artificial Intelligence, ICTAI, pp. 51–58 (2011)Google Scholar
  19. 19.
    Wang, H., Zhang, J., Wan, C., Shao, S., Cohen, R., Xu, J., Li, P.: Web service selection for multiple agents with incomplete preferences. In: Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT, pp. 565–572 (2010)Google Scholar
  20. 20.
    Wang, H., Zhou, X., Zhou, X., Liu, W., Li, W., Bouguettaya, A.: Adaptive Service Composition Based on Reinforcement Learning. In: Maglio, P.P., Weske, M., Yang, J., Fantinato, M. (eds.) ICSOC 2010. LNCS, vol. 6470, pp. 92–107. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  21. 21.
    Wilson, N.: Extending cp-nets with stronger conditional preference statements. In: Proceedings of the National Conference on Artificial Intelligence, pp. 735–741 (2004)Google Scholar
  22. 22.
    Xu, H., Hipel, K., Marc Kilgour, D.: Multiple levels of preference in interactive strategic decisions. Discrete Applied Mathematics 157(15), 3300–3313 (2009)MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Hongbing Wang
    • 1
  • Jie Zhang
    • 2
  • Wenlong Sun
    • 1
  • Hongye Song
    • 1
  • Guibing Guo
    • 2
  • Xiang Zhou
    • 1
  1. 1.School of Computer Science and EngineeringSoutheast UniversityNanjingChina
  2. 2.School of Computer EngineeringNanyang Technological UniversitySingapore

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