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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)

Abstract

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.

Keywords

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.

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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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